CN117073154A - Refrigerating air conditioner operation efficiency detecting system based on Internet of things - Google Patents
Refrigerating air conditioner operation efficiency detecting system based on Internet of things Download PDFInfo
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- CN117073154A CN117073154A CN202311209722.3A CN202311209722A CN117073154A CN 117073154 A CN117073154 A CN 117073154A CN 202311209722 A CN202311209722 A CN 202311209722A CN 117073154 A CN117073154 A CN 117073154A
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- 238000001514 detection method Methods 0.000 claims abstract description 110
- 238000005057 refrigeration Methods 0.000 claims abstract description 90
- 238000011156 evaluation Methods 0.000 claims abstract description 53
- 238000005265 energy consumption Methods 0.000 claims abstract description 43
- 238000011084 recovery Methods 0.000 claims abstract description 43
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000012795 verification Methods 0.000 claims abstract description 16
- 230000003044 adaptive effect Effects 0.000 claims abstract description 11
- 238000012423 maintenance Methods 0.000 claims description 51
- 239000003507 refrigerant Substances 0.000 claims description 49
- 238000004364 calculation method Methods 0.000 claims description 28
- 230000002159 abnormal effect Effects 0.000 claims description 24
- 238000013486 operation strategy Methods 0.000 claims description 15
- 238000004378 air conditioning Methods 0.000 claims description 7
- 238000007619 statistical method Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 10
- 230000009286 beneficial effect Effects 0.000 description 6
- 238000007689 inspection Methods 0.000 description 3
- 230000000903 blocking effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
- F24F11/47—Responding to energy costs
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/52—Indication arrangements, e.g. displays
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/89—Arrangement or mounting of control or safety devices
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- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention belongs to the technical field of air conditioner operation supervision, in particular to a refrigerating air conditioner operation efficiency detection system based on the Internet of things, which comprises an operation management and control platform, an operation parameter deviation verification module, an adaptive regulation and control module, a regulation and control restorability detection module and an operation comprehensive evaluation module; according to the invention, the deviation verification analysis is carried out in the operation process of the refrigeration air conditioner through the operation parameter deviation verification module, the operation parameter deviation condition of the refrigeration air conditioner can be accurately fed back, the operation process of the refrigeration air conditioner is subjected to the regulation and recovery detection analysis through the regulation and recovery detection module, the accurate judgment and feedback of the parameter regulation and control efficiency of the refrigeration air conditioner are realized, the current operation efficiency condition and the operation energy consumption condition of the refrigeration air conditioner are analyzed, the efficient, stable and energy-saving operation of the refrigeration air conditioner is effectively ensured, and the operation efficiency and the operation effect of the refrigeration air conditioner are remarkably improved.
Description
Technical Field
The invention relates to the technical field of air conditioner operation supervision, in particular to a refrigerating air conditioner operation efficiency detection system based on the Internet of things.
Background
An air conditioner is an air conditioner, which can perform operations such as heating, cooling, drying, dehumidifying and the like on air, so that the indoor temperature and humidity are kept at proper levels, a comfortable environment is provided, the air conditioner is usually composed of a refrigerant, a refrigerating unit, a radiator, a fan and the like, and the refrigerating air conditioner is mainly used for blowing out the cold air so as to reduce the indoor temperature, and is mostly used in hot summer;
at present, in the running process of a refrigeration air conditioner, the deviation condition of running parameters in the refrigerating operation process is difficult to accurately analyze and feed back and automatically adaptively correct, the parameter regulation and control efficiency is difficult to effectively analyze, the running condition cannot be comprehensively evaluated when the refrigerating operation is finished, the safety and the stability of the running condition are not favorable, and the running efficiency and the running effect are difficult to ensure;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a refrigerating air conditioner operation efficiency detection system based on the Internet of things, which solves the problems that in the prior art, the operation parameter deviation condition of a refrigerating air conditioner in the operation process is difficult to accurately analyze and feed back and automatically adaptively correct, the parameter regulation and control efficiency is difficult to effectively analyze and comprehensively evaluate the operation condition, and the operation efficiency and the operation effect of the refrigerating air conditioner are not easy to ensure.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a refrigerating air conditioner operation efficiency detection system based on the Internet of things comprises an operation control platform, an operation parameter deviation verification module, an adaptive regulation module, a regulation recovery detection module and an operation comprehensive evaluation module; the operation control platform is used for receiving operation mode data sent by a user, generating a corresponding refrigeration operation strategy and enabling the corresponding refrigeration air conditioner to perform refrigeration operation in the closed area based on the refrigeration operation strategy; the operation parameter deviation checking module is used for performing deviation checking analysis in the operation process of the refrigerating air conditioner so as to generate a deviation checking qualified signal or a deviation checking unqualified signal, and sending the deviation checking qualified signal or the deviation checking unqualified signal to the operation control platform;
the operation control platform sends the deviation checking disqualification signal to the adaptive regulation module, and the adaptive regulation module adaptively regulates the operation parameters of the refrigeration air conditioner when receiving the deviation checking disqualification signal; the regulation and control restorability detection module regulates and controls the operation process of the refrigeration air conditioner to carry out restorability detection analysis, so as to generate a restorability detection normal signal or a restorability detection abnormal signal, and sends the restorability detection normal signal or the restorability detection abnormal signal to the operation management and control platform;
the operation comprehensive evaluation module is used for analyzing the current operation efficiency condition of the refrigeration air conditioner, judging whether an operation efficiency evaluation disqualification signal is generated, analyzing the operation energy consumption condition if the operation efficiency evaluation disqualification signal is not generated, and judging whether the operation energy consumption evaluation disqualification signal is generated; and sending the operation efficiency evaluation disqualification signal or the operation energy consumption evaluation disqualification signal to an operation control platform, and sending the deviation checking disqualification signal, the restorability detection abnormal signal, the operation efficiency evaluation disqualification signal or the operation energy consumption evaluation disqualification signal to an early warning terminal by the operation control platform so as to enable the early warning terminal to send corresponding early warning.
Further, the specific analysis process of the deviation checking analysis comprises the following steps:
setting a checking period, setting a plurality of detection time points in the checking period, and marking the corresponding detection time points as i, i= {1,2, …, n }, wherein n represents the number of the detection time points and is a natural number larger than 5; acquiring a refrigerant pressure set value, a refrigerant temperature set value, a fan rotating speed set value and a refrigerant flow speed set value when the refrigerating air conditioner operates based on a refrigerating operation strategy, acquiring a refrigerant real-time pressure value, a refrigerant real-time temperature value, a fan real-time rotating speed value and a refrigerant real-time flow speed value of the refrigerating air conditioner at a detection time point i, performing difference calculation on the refrigerant real-time pressure value and the refrigerant pressure set value to obtain a refrigerant pressure meter value, and acquiring the refrigerant temperature meter value, the fan speed meter value and the refrigerant speed meter value of the refrigerating air conditioner at the detection time point in the same way;
performing numerical calculation on a refrigerant pressure table value, a refrigerant temperature table value, a fan speed table value and a refrigerant speed table value of a refrigeration air conditioner at a detection time point i to obtain an air conditioner deviation value, establishing a deviation value set of all air conditioner deviation values in a checking period, and performing mean value calculation and variance calculation on the deviation value set to obtain an air conditioner deviation value and an air conditioner deviation discrete value; respectively carrying out numerical comparison on the air conditioner deviation representing value and the air conditioner deviation discrete value with a preset air conditioner deviation representing threshold value and a preset air conditioner deviation discrete threshold value, and generating a deviation checking disqualification signal if the air conditioner deviation representing value exceeds the preset air conditioner deviation representing threshold value and the air conditioner deviation discrete value does not exceed the preset air conditioner deviation discrete threshold value; if the air conditioner deviation representing value does not exceed the preset air conditioner deviation representing threshold value and the air conditioner deviation discrete value does not exceed the preset air conditioner deviation discrete threshold value, generating a deviation checking qualified signal; and carrying out point-by-point statistical analysis on the rest conditions.
Further, the specific analysis process of the point-by-point statistical analysis is as follows:
comparing the air conditioner operation deviation value of the refrigerating air conditioner at the detection time point i with a preset air conditioner operation deviation threshold value, and judging that the refrigerating air conditioner at the detection time point i is in a high deviation state if the air conditioner operation deviation value exceeds the preset air conditioner operation deviation threshold value; counting the number of the refrigerating air conditioner in a high deviation state in the verification period, and calculating the ratio of the number in the high deviation state to the value n to obtain a high deviation state coefficient;
acquiring adjacent detection time points in a high deviation state, obtaining a maximum connecting value in the high deviation state according to the adjacent detection time points, sequencing subsets in a deflection value set according to the fact that the numbers are large to small, and marking the subset at the first position as a deflection high amplitude value; carrying out numerical calculation on a high deviation state coefficient, a high deviation state maximum joint value and a high deviation amplitude value of the refrigeration air conditioner in a checking period to obtain a deviation check value, and carrying out numerical comparison on the deviation check value and a preset deviation check value; if the deviation check value exceeds the preset deviation check threshold, generating a deviation check unqualified signal, and if the deviation check value does not exceed the preset deviation check threshold, generating a deviation check qualified signal.
Further, the specific analysis process of the regulation and control restorability detection analysis comprises the following steps:
when a deviation checking unqualified signal is generated, acquiring the time for generating the deviation checking unqualified signal, marking the time as checking unqualified time, acquiring the generation time of the deviation checking qualified signal adjacent to the time starting point by taking the checking unqualified time as time starting point, marking the time as adjacent checking qualified time, and calculating the time difference between the adjacent checking qualified time and the checking unqualified time to obtain the recovery interval duration;
collecting all recovery interval duration of the refrigerating air conditioner in unit time, comparing the recovery interval duration with a preset recovery interval duration threshold value, and marking the corresponding recovery interval duration as difficult recovery duration if the recovery interval duration exceeds the preset recovery interval duration threshold value; the method comprises the steps of obtaining the number of difficult-to-recover time periods in unit time, marking the difficult-to-recover time periods with the largest numerical value as a regulation and recovery high resistance value, and performing numerical calculation on the regulation and recovery resistance value and the regulation and recovery high resistance value to obtain a recovery detection value; and carrying out numerical comparison on the restorability detection value and a preset restorability detection threshold, generating a restorability detection abnormal signal if the restorability detection value exceeds the preset restorability detection threshold, and generating a restorability detection normal signal if the restorability detection value does not exceed the preset restorability detection threshold.
Further, the specific operation process of the operation comprehensive evaluation module comprises the following steps:
acquiring a preset refrigeration target temperature value, setting a plurality of temperature acquisition points in a closed area, summing the real-time temperatures of all the temperature acquisition points, and taking an average value to obtain an area real-time temperature value; the method comprises the steps that the time when a refrigerating air conditioner starts to operate according to a refrigerating operation strategy is collected and marked as a refrigerating initial time, the time when a real-time temperature value of an area reaches a refrigerating target temperature value is marked as a refrigerating standard reaching time, and the time difference between the refrigerating standard reaching time and the refrigerating initial time is calculated to obtain actual refrigerating duration;
obtaining a predicted refrigerating time length set value in a refrigerating operation strategy, subtracting a preset refrigerating time length threshold value from an actual refrigerating time length to obtain a refrigerating time length exceeding value, performing numerical comparison on the refrigerating time length exceeding value and the preset refrigerating time length exceeding threshold value, and generating an operation efficiency evaluation disqualification signal if the refrigerating time length exceeding value exceeds the preset refrigerating time length exceeding threshold value.
Further, if the refrigerating duration exceeding value does not exceed the preset refrigerating duration exceeding threshold value, acquiring the volume of the closed area and marking the volume as refrigerating area data, carrying out summation calculation on the initial temperatures of all the temperature acquisition points and taking an average value to obtain an area initial temperature value, and subtracting a refrigerating target temperature value from the area initial temperature value to obtain a refrigerating temperature lowering value;
and acquiring an electric quantity consumption value of the refrigerating air conditioner in the actual refrigerating time period, carrying out numerical calculation on the electric quantity consumption value, the refrigerating temperature reduction value and refrigerating area data to obtain an energy consumption analysis value, carrying out numerical comparison on the energy consumption analysis value and a preset energy consumption analysis threshold value, and generating an operation energy consumption evaluation disqualification signal if the energy consumption analysis value exceeds the preset energy consumption analysis threshold value.
Further, the operation control platform is in communication connection with the operation and maintenance feedback decision module, the operation and maintenance feedback decision module is used for obtaining the date of the last operation and maintenance adjacent, performing time difference calculation on the current date and the date of the last operation and maintenance adjacent to obtain operation and maintenance interval duration, obtaining an operation and maintenance interval duration through analysis, performing numerical calculation on the operation and maintenance interval duration to obtain an operation decision coefficient, performing numerical comparison on the operation decision coefficient and a preset operation decision coefficient threshold, generating an operation and maintenance early warning signal if the operation decision coefficient exceeds the preset operation decision coefficient threshold, and transmitting the operation and maintenance early warning signal to the early warning terminal through the operation control platform.
Further, the analysis and acquisition method of the operation feedback coefficient specifically comprises the following steps:
collecting the total operation time of the refrigerating air conditioner in the operation and maintenance interval time, collecting the generation times of the deviation check unqualified signals and the generation times of the restorability detection abnormal signals of the refrigerating air conditioner in the operation and maintenance interval time, marking the generation times as the deviation check unqualified frequency and the restorability abnormal frequency respectively, and collecting the generation times of the operation efficiency evaluation unqualified signals and the operation energy consumption evaluation unqualified signals and marking the generation times as the comprehensive evaluation unqualified frequency; and carrying out numerical calculation on the total operation time, the deviation checking unqualified frequency, the restorability abnormal frequency and the comprehensive evaluation unqualified frequency to obtain an operation and maintenance feedback coefficient.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the deviation verification analysis is carried out in the operation process of the refrigeration air conditioner through the operation parameter deviation verification module, the operation parameter deviation condition of the refrigeration air conditioner can be accurately analyzed and fed back so as to timely regulate and control the parameters of the refrigeration air conditioner, the regulation and recovery detection analysis is carried out on the operation process of the refrigeration air conditioner through the regulation and recovery detection module, the accurate judgment and feedback of the regulation and control efficiency of the parameters of the refrigeration air conditioner are realized, the regulation and control efficiency of the refrigeration air conditioner is well mastered by a manager, the current operation efficiency condition of the refrigeration air conditioner is analyzed, the operation energy consumption condition of the refrigeration air conditioner is analyzed when no operation efficiency evaluation unqualified signal is generated, the efficient and stable operation of the refrigeration air conditioner and the accurate feedback of the operation energy consumption condition are effectively ensured, and the operation efficiency and the operation effect of the refrigeration air conditioner are remarkably improved;
2. according to the invention, the operation and maintenance interval duration is acquired through the operation and maintenance feedback decision module, the operation and maintenance feedback coefficient in the operation and maintenance interval duration is acquired through analysis, so that the operation decision coefficient of the refrigeration air conditioner is obtained, whether an operation and maintenance early warning signal is generated or not is judged, the operation and maintenance early warning signal is sent to the early warning terminal through the operation management and control platform, and the corresponding early warning is sent out when the early warning terminal receives the operation and maintenance early warning signal, so that a manager is reminded to perform comprehensive inspection and maintenance of the refrigeration air conditioner in time, the subsequent safe, stable and efficient operation of the refrigeration air conditioner is ensured, and the service life of the refrigeration air conditioner is prolonged.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a system block diagram of a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1, the refrigerating and air conditioning operation efficiency detection system based on the internet of things provided by the invention comprises an operation control platform, an operation parameter deviation checking module, an adaptive regulation module, a regulation and recovery detection module and an operation comprehensive evaluation module, wherein the operation control platform is in communication connection with the operation parameter deviation checking module, the adaptive regulation module, the regulation and recovery detection module and the operation comprehensive evaluation module; the operation control platform is used for receiving operation mode data sent by a user, generating a corresponding refrigeration operation strategy and enabling the corresponding refrigeration air conditioner to perform refrigeration operation in the closed area based on the refrigeration operation strategy;
the operation parameter deviation checking module is used for performing deviation checking analysis in the operation process of the refrigeration air conditioner, so as to generate a deviation checking qualified signal or a deviation checking unqualified signal, and the deviation checking qualified signal or the deviation checking unqualified signal is sent to the operation control platform, so that the operation parameter deviation condition of the refrigeration air conditioner can be accurately analyzed and fed back, the parameter regulation of the refrigeration air conditioner can be performed in time, and the operation effect and the operation efficiency of the refrigeration air conditioner are ensured; the specific analysis process of the deviation checking analysis is as follows:
setting a checking period, setting a plurality of detection time points in the checking period, and marking the corresponding detection time points as i, i= {1,2, …, n }, wherein n represents the number of the detection time points and is a natural number larger than 5; acquiring a refrigerant pressure set value, a refrigerant temperature set value, a fan rotating speed set value and a refrigerant flow speed set value when the refrigerating air conditioner operates based on a refrigerating operation strategy, acquiring a refrigerant real-time pressure value, a refrigerant real-time temperature value, a fan real-time rotating speed value and a refrigerant real-time flow speed value of the refrigerating air conditioner at a detection time point i, performing difference calculation on the refrigerant real-time pressure value and the refrigerant pressure set value to obtain a refrigerant pressure meter value, and acquiring the refrigerant temperature meter value, the fan speed meter value and the refrigerant speed meter value of the refrigerating air conditioner at the detection time point in the same way; the larger the values of the refrigerant pressure meter value, the refrigerant temperature meter value, the fan speed meter value and the refrigerant speed meter value are, the larger the operation deviation degree of the corresponding parameters of the refrigeration air conditioner is, and the more abnormal the operation is;
performing numerical calculation on a refrigerant pressure table value LBi, a refrigerant temperature table value LWi, a fan speed table value FSi and a refrigerant speed table value LSi of the refrigeration air conditioner at a detection time point i through a formula KPi=eg1×LBi+eg2× LWi +eg3+FSi+eg4×LSi to obtain an air conditioner operation bias value KPi; wherein, eg1, eg2, eg3, eg4 are preset weight coefficients, and the values of eg1, eg2, eg3, eg4 are all larger than zero; the value of the air conditioner operation deviation value KPi is in a direct proportion relation with the refrigerant pressure meter value LBi, the refrigerant temperature meter value LWi, the fan speed meter value FSi and the refrigerant speed meter value LSi, and the larger the value of the air conditioner operation deviation value KPi is, the worse the running condition of the refrigeration air conditioner is, and the greater the running deviation degree is;
establishing an operation deviation value set for all the air conditioner operation deviation values in the checking period, and carrying out mean value calculation and variance calculation on the operation deviation value set to obtain an air conditioner operation deviation representing value and an air conditioner operation deviation discrete value; respectively carrying out numerical comparison on the air conditioner deviation representing value and the air conditioner deviation discrete value with a preset air conditioner deviation representing threshold value and a preset air conditioner deviation discrete threshold value, and if the air conditioner deviation representing value exceeds the preset air conditioner deviation representing threshold value and the air conditioner deviation discrete value does not exceed the preset air conditioner deviation discrete threshold value, indicating that the operation deviation degree of the refrigerating air conditioner is larger in the verification period as a whole, generating a deviation verification disqualification signal; if the air conditioner operation deviation representing value does not exceed the preset air conditioner operation deviation representing threshold value and the air conditioner operation deviation discrete value does not exceed the preset air conditioner operation deviation discrete threshold value, the operation deviation degree of the refrigerating air conditioner in the verification period is smaller as a whole, and a deviation verification qualified signal is generated;
and carrying out point-by-point statistical analysis on the rest conditions, wherein the method specifically comprises the following steps: comparing the air conditioner operation deviation value of the refrigeration air conditioner at the detection time point i with a preset air conditioner operation deviation threshold value, and judging that the refrigeration air conditioner at the detection time point i is in a high deviation state if the air conditioner operation deviation value exceeds the preset air conditioner operation deviation threshold value, which indicates that the operation deviation degree of the refrigeration air conditioner at the corresponding detection time point i is large; counting the number of the refrigerating air conditioner in a high deviation state in the verification period, and calculating the ratio of the number in the high deviation state to the value n to obtain a high deviation state coefficient;
acquiring adjacent detection time points in a high deviation state, obtaining a maximum connection value in the high deviation state (namely, the maximum number of the detection time points in the adjacent high deviation state) according to the adjacent detection time points in the high deviation state, sequencing subsets in a shipping deviation value set according to the number of the subsets, and marking the subset at the first position as a shipping deviation high amplitude; carrying out numerical calculation on a high deviation state coefficient GX, a high deviation state maximum engagement value GF and a high deviation amplitude GT of a refrigeration air conditioner in a verification period through a formula PY=tp1, GX+tp2, GF+tp3, wherein tp1, tp2 and tp3 are preset weight coefficients, and tp2 is larger than tp1 and tp3 is larger than 1;
and, the larger the value of the deviation check value PY, the worse the running condition of the refrigeration air conditioner in the check period as a whole is indicated; comparing the deviation check value PY with a preset deviation check value; if the deviation checking value PY exceeds the preset deviation checking threshold value, generating a deviation checking unqualified signal, and if the deviation checking value PY does not exceed the preset deviation checking threshold value, generating a deviation checking qualified signal.
The operation management and control platform sends the deviation checking disqualification signal to the adaptive regulation and control module, and the adaptive regulation and control module adaptively regulates the operation parameters of the refrigeration air conditioner when receiving the deviation checking disqualification signal, so that the parameter correction during the operation of the refrigeration air conditioner is realized, and the efficient and stable operation of the refrigeration air conditioner is facilitated; the regulation and control restorability detection module carries out regulation and control restorability detection analysis on the operation process of the refrigeration air conditioner so as to generate a restorability detection normal signal or a restorability detection abnormal signal, and sends the restorability detection normal signal or the restorability detection abnormal signal to the operation management and control platform, so that accurate judgment and feedback on the parameter regulation and control efficiency of the refrigeration air conditioner are realized, management staff can master the regulation and control efficiency of the refrigeration air conditioner in detail, corresponding improvement measures are timely made according to the requirement, and the operation efficiency and the operation effect of the refrigeration air conditioner are further ensured; the specific analysis process of the regulation and control restorability detection analysis is as follows:
when a deviation checking unqualified signal is generated, acquiring the time for generating the deviation checking unqualified signal, marking the time as checking unqualified time, acquiring the generation time of the deviation checking qualified signal adjacent to the time starting point by taking the checking unqualified time as time starting point, marking the time as adjacent checking qualified time, and calculating the time difference between the adjacent checking qualified time and the checking unqualified time to obtain the recovery interval duration; it should be noted that, the larger the value of the recovery interval duration is, the slower the regulation efficiency is;
collecting all recovery interval duration of the refrigerating air conditioner in unit time, comparing the recovery interval duration with a preset recovery interval duration threshold value, and marking the corresponding recovery interval duration as difficult recovery duration if the recovery interval duration exceeds the preset recovery interval duration threshold value; the method comprises the steps of obtaining the number of difficult-to-recover time periods in unit time, marking the number as a regulation and recovery blocking value, and marking the difficult-to-recover time period with the largest value as a regulation and recovery high resistance value;
performing numerical calculation on the regulation recovery blocking value TH and the regulation recovery high-resistance value TF through a formula HF=rq1+TH+rq2, and obtaining a recovery detection value HF, wherein rq1 and rq2 are preset weight coefficients, and rq1 is larger than rq2 and larger than 0; and, the larger the value of the restorability detection value HF is, the worse the regulating efficiency of the refrigerating air conditioner is indicated as a whole; performing numerical comparison on the restorability detection value HF and a preset restorability detection threshold, and generating a restorability detection abnormal signal if the restorability detection value HF exceeds the preset restorability detection threshold; and if the restorability detection value HF does not exceed the preset restorability detection threshold, generating a restorability detection normal signal.
The operation comprehensive evaluation module is used for analyzing the current operation efficiency condition of the refrigeration air conditioner, judging whether an operation efficiency evaluation disqualification signal is generated, analyzing the operation energy consumption condition if the operation efficiency evaluation disqualification signal is not generated, judging whether the operation energy consumption evaluation disqualification signal is generated, and sending the operation efficiency evaluation disqualification signal or the operation energy consumption evaluation disqualification signal to the operation control platform to realize accurate feedback of the operation efficiency and the operation energy consumption condition of the refrigeration air conditioner, thereby being beneficial to timely finding out the abnormal efficiency and energy consumption of the refrigeration air conditioner and timely making corresponding improvement measures according to the requirement, and further being beneficial to ensuring the efficient, stable and energy-saving operation of the refrigeration air conditioner; the specific operation process of the operation comprehensive evaluation module is as follows:
acquiring a preset refrigeration target temperature value, setting a plurality of temperature acquisition points in a closed area, summing the real-time temperatures of all the temperature acquisition points, and taking an average value to obtain an area real-time temperature value; the method comprises the steps that the time when a refrigerating air conditioner starts to operate according to a refrigerating operation strategy is collected and marked as a refrigerating initial time, the time when a real-time temperature value of an area reaches a refrigerating target temperature value is marked as a refrigerating standard reaching time, and the time difference between the refrigerating standard reaching time and the refrigerating initial time is calculated to obtain actual refrigerating duration;
obtaining a predicted refrigerating time length set value in a refrigerating operation strategy, subtracting a preset refrigerating time length threshold value from an actual refrigerating time length to obtain a refrigerating time length exceeding value, wherein the larger the value of the refrigerating time length exceeding value is, the slower the refrigerating efficiency of the refrigerating air conditioner is; and comparing the refrigerating duration exceeding value with a preset refrigerating duration exceeding threshold value in a numerical mode, and if the refrigerating duration exceeding value exceeds the preset refrigerating duration exceeding threshold value, indicating that the refrigerating efficiency of the refrigerating air conditioner is slower, generating an operation efficiency evaluation disqualification signal.
Further, if the refrigerating duration exceeding value does not exceed the preset refrigerating duration exceeding threshold value, acquiring the volume of the closed area and marking the volume as refrigerating area data, summing the initial temperatures of all the temperature acquisition points, taking an average value to obtain an area initial temperature value, and subtracting a refrigerating target temperature value from the area initial temperature value to obtain a refrigerating temperature drop value; collecting an electric quantity consumption value of a refrigerating air conditioner in an actual refrigerating time period, and carrying out numerical calculation on the electric quantity consumption value LX, a refrigerating temperature reduction value LJ and refrigerating area data ZQ through a formula KF=fp1/(fp 2 LJ+fp3 ZQ) to obtain an energy consumption analysis value KF;
wherein fp1, fp2 and fp3 are preset proportionality coefficients, and the values of fp1, fp2 and fp3 are all larger than zero; and the larger the value of the energy consumption analysis value KF is, the larger the energy consumption of the refrigeration operation of the refrigeration air conditioner is, and the worse the operation effect of the refrigeration air conditioner is; comparing the energy consumption analysis value KF with a preset energy consumption analysis threshold value in a numerical mode, and generating an operation energy consumption evaluation disqualification signal if the energy consumption analysis value KF exceeds the preset energy consumption analysis threshold value; if the energy consumption analysis value KF does not exceed the preset energy consumption analysis threshold value, judging that the operation effect of the refrigerating air conditioner is good.
The operation management and control platform sends the deviation checking disqualification signal, the restorability detection abnormal signal, the operation efficiency evaluation disqualification signal or the operation energy consumption evaluation disqualification signal to the early warning terminal, and the early warning terminal sends corresponding early warning when receiving the related signals so as to remind a manager to conduct reason investigation and tracing according to the needs and conduct inspection and maintenance on related parts of the refrigeration air conditioner according to the needs, thereby being beneficial to ensuring safe, stable and efficient operation of the refrigeration air conditioner and improving the operation efficiency and the operation effect of the refrigeration air conditioner.
Embodiment two: as shown in fig. 2, the difference between the present embodiment and embodiment 1 is that the operation control platform is in communication connection with an operation and maintenance feedback decision module, and the operation and maintenance feedback decision module is configured to obtain a date of a last operation and maintenance adjacent to the last operation and maintenance, calculate a time difference between a current date and the date of the last operation and maintenance adjacent to the last date to obtain an operation and maintenance interval duration, and obtain an operation feedback coefficient in the operation and maintenance interval duration through analysis, where the operation and maintenance interval duration is specifically: collecting the total operation time length of the refrigerating air conditioner in the operation and maintenance interval time length (namely the total refrigeration operation time length of the refrigerating air conditioner), collecting the generation times of deviation check unqualified signals and the generation times of restorability detection abnormal signals of the refrigerating air conditioner in the operation and maintenance interval time length, respectively marking the generation times as deviation check unqualified frequency and restorability abnormal frequency, and collecting the generation times of operation efficiency evaluation unqualified signals and operation energy consumption evaluation unqualified signals and marking the generation times as comprehensive evaluation unqualified frequency;
carrying out numerical calculation on the total operation duration YS, the deviation verification failure frequency PB, the restorative abnormal frequency HB and the comprehensive evaluation failure frequency ZB through a formula YF= (a2+a3+a4+ZB)/(a1+YS+0.935), wherein a1, a2, a3 and a4 are preset proportionality coefficients, and a3 > a4 > a2 > a1 > 0; in addition, the larger the value of the operation and maintenance feedback coefficient YF is, the worse the operation condition of the refrigeration air conditioner in the operation and maintenance interval duration is, and the more the refrigeration air conditioner needs to be comprehensively checked and maintained in time;
performing numerical calculation on an operation feedback coefficient YF and an operation and maintenance interval duration YC through a formula YJ=b1 xYF+b2 xYC to obtain an operation decision coefficient YJ, wherein b1 and b2 are preset weight coefficients, and b1 is more than b2 is more than 0; and the larger the value of the operation decision coefficient YJ is, the more the refrigerating air conditioner needs to be comprehensively checked and maintained in time; comparing the operation decision coefficient YJ with a preset operation decision coefficient threshold value, if the operation decision coefficient YJ exceeds the preset operation decision coefficient threshold value, generating an operation and maintenance early warning signal, and transmitting the operation and maintenance early warning signal to an early warning terminal through an operation management and control platform; the early warning terminal sends out corresponding early warning when receiving the operation and maintenance early warning signal to remind the manager to carry out comprehensive inspection and maintenance of the refrigeration air conditioner in time, thereby guaranteeing the follow-up safe, stable and efficient operation of the refrigeration air conditioner and being beneficial to improving the service life of the refrigeration air conditioner.
The working principle of the invention is as follows: when the system is used, the deviation checking module performs deviation checking analysis in the operation process of the refrigerating air conditioner, so that a deviation checking qualified signal or a deviation checking unqualified signal is generated, and the operation parameter deviation condition of the refrigerating air conditioner can be accurately analyzed and fed back; the operation process of the refrigeration air conditioner is subjected to regulation and control restorability detection analysis through the regulation and control restorability detection module, so that a restorability detection normal signal or a restorability detection abnormal signal is generated, the accurate judgment and feedback of the regulation and control efficiency of the refrigeration air conditioner parameters are realized, and management staff can master the regulation and control efficiency of the refrigeration air conditioner in detail; and the operation comprehensive evaluation module is used for analyzing the current operation efficiency condition of the refrigeration air conditioner, analyzing the operation energy consumption condition if no operation efficiency evaluation disqualification signal is generated, realizing the accurate feedback of the operation efficiency and the operation energy consumption condition of the refrigeration air conditioner, being beneficial to timely finding out the abnormality of the efficiency and the energy consumption of the refrigeration air conditioner, timely making corresponding improvement measures according to the needs, effectively ensuring the efficient, stable and energy-saving operation of the refrigeration air conditioner, and improving the operation efficiency and the operation effect of the refrigeration air conditioner.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The refrigerating air conditioner operation efficiency detection system based on the Internet of things is characterized by comprising an operation control platform, an operation parameter deviation verification module, an adaptive regulation and control module, a regulation and control restorability detection module and an operation comprehensive evaluation module; the operation control platform is used for receiving operation mode data sent by a user, generating a corresponding refrigeration operation strategy and enabling the corresponding refrigeration air conditioner to perform refrigeration operation in the closed area based on the refrigeration operation strategy; the operation parameter deviation checking module is used for performing deviation checking analysis in the operation process of the refrigerating air conditioner so as to generate a deviation checking qualified signal or a deviation checking unqualified signal, and sending the deviation checking qualified signal or the deviation checking unqualified signal to the operation control platform;
the operation control platform sends the deviation checking disqualification signal to the adaptive regulation module, and the adaptive regulation module adaptively regulates the operation parameters of the refrigeration air conditioner when receiving the deviation checking disqualification signal; the regulation and control restorability detection module regulates and controls the operation process of the refrigeration air conditioner to carry out restorability detection analysis, so as to generate a restorability detection normal signal or a restorability detection abnormal signal, and sends the restorability detection normal signal or the restorability detection abnormal signal to the operation management and control platform;
the operation comprehensive evaluation module is used for analyzing the current operation efficiency condition of the refrigeration air conditioner, judging whether an operation efficiency evaluation disqualification signal is generated, analyzing the operation energy consumption condition if the operation efficiency evaluation disqualification signal is not generated, and judging whether the operation energy consumption evaluation disqualification signal is generated; and sending the operation efficiency evaluation disqualification signal or the operation energy consumption evaluation disqualification signal to an operation control platform, and sending the deviation checking disqualification signal, the restorability detection abnormal signal, the operation efficiency evaluation disqualification signal or the operation energy consumption evaluation disqualification signal to an early warning terminal by the operation control platform so as to enable the early warning terminal to send corresponding early warning.
2. The refrigerating and air-conditioning operation efficiency detection system based on the internet of things according to claim 1, wherein the specific analysis process of the deviation checking analysis comprises:
setting a checking period, setting a plurality of detection time points in the checking period, and marking the corresponding detection time points as i, i= {1,2, …, n }, wherein n represents the number of the detection time points and is a natural number larger than 5; acquiring a refrigerant pressure set value, a refrigerant temperature set value, a fan rotating speed set value and a refrigerant flow speed set value when the refrigerating air conditioner operates based on a refrigerating operation strategy, acquiring a refrigerant real-time pressure value, a refrigerant real-time temperature value, a fan real-time rotating speed value and a refrigerant real-time flow speed value of the refrigerating air conditioner at a detection time point i, performing difference calculation on the refrigerant real-time pressure value and the refrigerant pressure set value to obtain a refrigerant pressure meter value, and acquiring the refrigerant temperature meter value, the fan speed meter value and the refrigerant speed meter value of the refrigerating air conditioner at the detection time point in the same way;
performing numerical calculation on a refrigerant pressure table value, a refrigerant temperature table value, a fan speed table value and a refrigerant speed table value of a refrigeration air conditioner at a detection time point i to obtain an air conditioner deviation value, establishing a deviation value set of all air conditioner deviation values in a checking period, and performing mean value calculation and variance calculation on the deviation value set to obtain an air conditioner deviation value and an air conditioner deviation discrete value; if the air conditioner deviation performance value exceeds a preset air conditioner deviation performance threshold value and the air conditioner deviation discrete value does not exceed the preset air conditioner deviation discrete threshold value, generating a deviation checking disqualification signal; if the air conditioner deviation representing value does not exceed the preset air conditioner deviation representing threshold value and the air conditioner deviation discrete value does not exceed the preset air conditioner deviation discrete threshold value, generating a deviation checking qualified signal; and carrying out point-by-point statistical analysis on the rest conditions.
3. The refrigerating and air-conditioning operation efficiency detection system based on the internet of things according to claim 2, wherein the specific analysis process of the point-by-point statistical analysis is as follows:
comparing the air conditioner operation deviation value of the refrigerating air conditioner at the detection time point i with a preset air conditioner operation deviation threshold value, and judging that the refrigerating air conditioner at the detection time point i is in a high deviation state if the air conditioner operation deviation value exceeds the preset air conditioner operation deviation threshold value; counting the number of the refrigerating air conditioner in a high deviation state in the verification period, and calculating the ratio of the number in the high deviation state to the value n to obtain a high deviation state coefficient;
acquiring adjacent detection time points in a high deviation state, obtaining a maximum connecting value in the high deviation state according to the adjacent detection time points, sequencing subsets in a deflection value set according to the fact that the numbers are large to small, and marking the subset at the first position as a deflection high amplitude value; carrying out numerical calculation on a high deviation state coefficient, a high deviation state maximum engagement value and a high deviation operation amplitude value of the refrigeration air conditioner in the checking period to obtain a deviation checking value; if the deviation check value exceeds the preset deviation check threshold, generating a deviation check unqualified signal, and if the deviation check value does not exceed the preset deviation check threshold, generating a deviation check qualified signal.
4. The refrigerating and air-conditioning operation efficiency detection system based on the internet of things according to claim 1, wherein the specific analysis process of the regulation and control restorability detection analysis comprises:
when a deviation checking unqualified signal is generated, acquiring the time for generating the deviation checking unqualified signal, marking the time as checking unqualified time, acquiring the generation time of the deviation checking qualified signal adjacent to the time starting point by taking the checking unqualified time as time starting point, marking the time as adjacent checking qualified time, and calculating the time difference between the adjacent checking qualified time and the checking unqualified time to obtain the recovery interval duration;
collecting all recovery interval duration of the refrigerating air conditioner in unit time, and marking the corresponding recovery interval duration as difficult recovery duration if the recovery interval duration exceeds a preset recovery interval duration threshold; the method comprises the steps of obtaining the number of difficult-to-recover time periods in unit time, marking the difficult-to-recover time periods with the largest numerical value as a regulation and recovery high resistance value, and performing numerical calculation on the regulation and recovery resistance value and the regulation and recovery high resistance value to obtain a recovery detection value; if the restorability detection value exceeds the preset restorability detection threshold, generating a restorability detection abnormal signal, and if the restorability detection value does not exceed the preset restorability detection threshold, generating a restorability detection normal signal.
5. The refrigerating and air-conditioning operation efficiency detection system based on the internet of things according to claim 1, wherein the specific operation process of operating the comprehensive evaluation module comprises:
acquiring a preset refrigeration target temperature value, setting a plurality of temperature acquisition points in a closed area, summing the real-time temperatures of all the temperature acquisition points, and taking an average value to obtain an area real-time temperature value; the method comprises the steps that the time when a refrigerating air conditioner starts to operate according to a refrigerating operation strategy is collected and marked as a refrigerating initial time, the time when a real-time temperature value of an area reaches a refrigerating target temperature value is marked as a refrigerating standard reaching time, and the time difference between the refrigerating standard reaching time and the refrigerating initial time is calculated to obtain actual refrigerating duration;
obtaining a predicted refrigerating time length set value in a refrigerating operation strategy, and subtracting a preset refrigerating time length threshold value from the actual refrigerating time length to obtain a refrigerating time length exceeding value; and if the refrigerating duration exceeding value exceeds the preset refrigerating duration exceeding threshold value, generating an operation efficiency evaluation disqualification signal.
6. The system for detecting the operation efficiency of the refrigerating air conditioner based on the Internet of things according to claim 5, wherein if the refrigerating duration exceeding value does not exceed the preset refrigerating duration exceeding threshold value, the volume of the closed area is obtained and marked as refrigerating area data, the initial temperatures of all the temperature acquisition points are summed up and averaged to obtain an area initial temperature value, and the refrigerating target temperature value is subtracted from the area initial temperature value to obtain a refrigerating temperature reduction value; acquiring an electric quantity consumption value of the refrigerating air conditioner in the actual refrigerating time period, and carrying out numerical calculation on the electric quantity consumption value, the refrigerating temperature reduction value and refrigerating area data to obtain an energy consumption analysis value; and if the energy consumption analysis value exceeds a preset energy consumption analysis threshold value, generating an operation energy consumption evaluation disqualification signal.
7. The refrigerating and air-conditioning operation efficiency detection system based on the Internet of things according to claim 1, wherein the operation control platform is in communication connection with an operation and maintenance feedback decision module, the operation and maintenance feedback decision module is used for acquiring the date of the last operation and maintenance adjacent to the date of the last operation and maintenance, calculating the time difference between the current date and the date of the last operation and maintenance to obtain operation and maintenance interval time, analyzing the operation and feedback coefficient acquired in the operation and maintenance interval time, and calculating the operation and maintenance interval time to obtain the operation and decision coefficient; if the operation decision coefficient exceeds a preset operation decision coefficient threshold, an operation and maintenance early warning signal is generated, and the operation and maintenance early warning signal is sent to an early warning terminal through an operation management and control platform.
8. The refrigerating and air-conditioning operation efficiency detection system based on the internet of things according to claim 7, wherein the analysis and acquisition method of the operation feedback coefficient is specifically as follows:
collecting the total operation time of the refrigerating air conditioner in the operation and maintenance interval time, collecting the generation times of the deviation check unqualified signals and the generation times of the restorability detection abnormal signals of the refrigerating air conditioner in the operation and maintenance interval time, marking the generation times as the deviation check unqualified frequency and the restorability abnormal frequency respectively, and collecting the generation times of the operation efficiency evaluation unqualified signals and the operation energy consumption evaluation unqualified signals and marking the generation times as the comprehensive evaluation unqualified frequency; and carrying out numerical calculation on the total operation time, the deviation checking unqualified frequency, the restorability abnormal frequency and the comprehensive evaluation unqualified frequency to obtain an operation and maintenance feedback coefficient.
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| CN117406685A (en) * | 2023-12-14 | 2024-01-16 | 无锡泰禾宏科技有限公司 | Intelligent control optimizing management system of building equipment suitable for green low-carbon building |
| CN117687326A (en) * | 2024-02-02 | 2024-03-12 | 无锡南方声学工程有限公司 | Ultrasonic cleaner operation monitoring and early warning system based on data analysis |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117406685A (en) * | 2023-12-14 | 2024-01-16 | 无锡泰禾宏科技有限公司 | Intelligent control optimizing management system of building equipment suitable for green low-carbon building |
| CN117406685B (en) * | 2023-12-14 | 2024-03-08 | 无锡泰禾宏科技有限公司 | Intelligent control optimizing management system of building equipment suitable for green low-carbon building |
| CN117687326A (en) * | 2024-02-02 | 2024-03-12 | 无锡南方声学工程有限公司 | Ultrasonic cleaner operation monitoring and early warning system based on data analysis |
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