CN117608255B - Remote monitoring management system and method for intelligent BA automatic control system of new energy factory - Google Patents
Remote monitoring management system and method for intelligent BA automatic control system of new energy factory Download PDFInfo
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Abstract
The invention discloses a remote monitoring management system and a remote monitoring management method for a BA (automatic dependent control) automatic control system of a new energy intelligent factory, which belong to the technical field of intelligent factory monitoring, wherein intelligent factory areas are divided, real-time temperature values of each sub-area are obtained and marked, external temperature change values, the number of people in each sub-area and corresponding sub-area areas are obtained, and S30 is carried out or S40 is carried out according to the judgment of the sub-area areas.
Description
Technical Field
The invention belongs to the technical field of intelligent factory monitoring, and particularly relates to a remote monitoring management system and method of a BA automatic control system of a new energy intelligent factory.
Background
The intelligent factory is also called a digital factory, which is mainly based on a product life cycle (PLM), realizes the interconnection of equipment of the factory through the technology of the Internet of things, and integrates the data (such as MES) of a control layer with an enterprise information system (ERP) to collect big data of production, and transmits the big data to a cloud computing data center for storage and analysis, early warning and decision making and conversely guiding production.
For example, the application publication number is: the patent of CN114738938A discloses a multi-split air conditioning unit fault monitoring method, a device and a storage medium, and aims to solve the technical problems that the existing fault monitoring method is low in execution efficiency and poor in fault judging accuracy. For this purpose, the fault monitoring method of the multi-split air conditioning unit of the invention comprises the following steps: collecting unit parameter information of a historical multi-split air conditioner unit, wherein the unit parameter information corresponds to a fault position label; constructing a fault prediction model; training a fault prediction model by utilizing unit parameter information of a historical multi-split air conditioner unit to obtain a trained fault prediction model; and predicting the fault position of the multi-split air conditioner unit by using the trained fault prediction model. In this way, the efficiency and accuracy of failure prediction are improved, but the following problems also exist:
in the prior art, mechanical equipment in the intelligent factory is usually provided with a state monitoring unit, the abnormality of the mechanical equipment can be found in time, the abnormality of the temperature in the factory is usually an air conditioning unit failure, if the scale of a park is larger, the number of factories in the park is more, the factories need to install a large amount of air conditioning units, then the intelligent factory management system monitors the working states of all the air conditioning units simultaneously, and calculates acquired data, so that the load of the operation of the intelligent factory management system is increased undoubtedly, and the operation of the intelligent factory management system is easy to fail.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
In order to solve the problems, the invention adopts the following technical scheme.
A remote monitoring management method of a BA automatic control system of a new energy intelligent factory comprises the following steps:
s10: dividing intelligent factory areas, acquiring real-time temperature values of each sub-area, and marking, wherein the intelligent factory areas are factory building internal areas;
s20: acquiring an external temperature change value, the number of people in each sub-area and the corresponding sub-area, and judging to switch to S30 or S40 according to the sub-area, wherein the external temperature change value is a temperature difference value of the factory building in unit time;
s30: inputting the real-time temperature value and the external temperature change value into an internal temperature correction model, obtaining an internal accurate temperature value output by the internal temperature correction model, and turning to S50;
s40: generating a temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number, and switching to S50;
s50: determining a target subregion based on the internal accurate temperature value or the temperature influence coefficient;
s60: acquiring working state data of all air conditioning units in a target subarea, and converting the working state data into standard state data according to a preset data conversion method, wherein the working state data comprise fan frequency, return air temperature, water valve frequency and filter screen pressure difference;
s70: and inputting the standard state data into a unit fault discrimination model, obtaining fault types output by the unit fault discrimination model and feeding back the fault types to a control background, wherein the fault types comprise overload, blockage, water leakage and refrigerant shortage.
Preferably, the personnel number acquisition method of each sub-area is as follows: the entrance guard management system is used for counting the number of people and tracking the positions of operators, so that the automatic record of personnel entering and exiting is realized, and the number of the operators is counted accurately in real time.
Preferably, the logic for determining to switch to S30 or S40 according to the subarea area is:
when the area of the sub-area is larger than the area of the preset area, the step S30 is carried out;
and when the area of the sub-area is smaller than or equal to the area of the preset area, switching to S40.
Preferably, the training process of the internal temperature correction model is: the method comprises the steps of obtaining a sample data set, wherein the sample data set comprises a real-time temperature value, an external temperature change value and an internal accurate temperature value, dividing the sample data set into a sample training set and a sample testing set, constructing a regression network, taking the real-time temperature value and the external temperature change value in the sample training set as input data of the regression network, taking the internal accurate temperature value in the sample training set as output data of the regression network, training the regression network to obtain an initial regression network for predicting the internal accurate temperature value, testing the initial regression network by utilizing the sample testing set, and outputting the regression network meeting the preset test accuracy as an internal temperature correction model.
Preferably, the method of generating the temperature influence coefficient according to the real-time temperature value, the external temperature variation value and the number of persons includes:
；
in the method, in the process of the invention,is->Temperature influence coefficient of sub-region, +.>Is->Real-time temperature values for the individual sub-zones,is->Standard temperature value of sub-region, +.>Is->External temperature variation value of sub-region, +.>Is->Number of people in sub-area->、/>And->Are all weight factors.
Preferably, the logic for determining the target sub-region based on the internal accurate temperature value comprises:
when the internal accurate temperature value is larger than or equal to a first preset internal temperature threshold value and smaller than or equal to a second preset internal temperature threshold value, marking a subarea corresponding to the internal accurate temperature value as a target subarea, wherein the second preset internal temperature threshold value is larger than the first preset internal temperature threshold value;
when the internal accurate temperature value is smaller than the first preset internal temperature threshold value or larger than the second preset internal temperature threshold value, marking the subarea corresponding to the internal accurate temperature value as a target subarea.
Preferably, the logic for determining the target sub-region based on the temperature influence coefficient comprises:
when the temperature influence coefficient is larger than or equal to a preset influence coefficient, marking a subarea corresponding to the internal accurate temperature value as a target subarea;
when the temperature influence coefficient is smaller than the preset influence coefficient, the subarea corresponding to the temperature influence coefficient is not marked as a target subarea.
Preferably, the data conversion method includes:
s601: determining the data type and the data unit of the working state data;
s602: judging whether the data unit is a standard unit, if so, replacing the working state data with the standard state data for output, and if not, turning to S603;
s603: and converting the data unit into a standard unit through a unit conversion algorithm, and replacing the working state data with the standard state data for output.
Preferably, the training process of the unit fault discrimination model is as follows: obtaining multiple groups of data, wherein the data comprise standard state data and fault categories, taking the standard state data and the fault categories as sample sets, dividing the sample sets into training sets and test sets, constructing a classifier, taking the standard state data in the training sets as input data, taking the fault categories in the training sets as output data, training the classifier to obtain an initial classifier, testing the initial classifier by using the test sets, and outputting the classifier meeting the preset accuracy as a unit fault judging model.
The remote monitoring management system of the intelligent BA automatic control system of the new energy factory is used for realizing the remote monitoring management method of the intelligent BA automatic control system of the new energy factory, and comprises the following steps:
and a data acquisition module: dividing intelligent factory areas, acquiring real-time temperature values of each sub-area, and marking, wherein the intelligent factory areas are factory building internal areas;
and a judging module: acquiring an external temperature change value, the number of people in each sub-area and the corresponding sub-area, and judging whether to switch to a temperature correction module or a coefficient generation module according to the sub-area, wherein the external temperature change value is a temperature difference value of the factory building in unit time;
and a temperature correction module: inputting the real-time temperature value and the external temperature change value into an internal temperature correction model, obtaining an internal accurate temperature value output by the internal temperature correction model, and transferring to a region determining module;
and a coefficient generation module: generating a temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number, and transferring to a region determining module;
the area determination module: determining a target subregion based on the internal accurate temperature value or the temperature influence coefficient;
and a conversion module: acquiring working state data of all air conditioning units in a target subarea, and converting the working state data into standard state data according to a preset data conversion method, wherein the working state data comprise fan frequency, return air temperature, water valve frequency and filter screen pressure difference;
and a feedback module: and inputting the standard state data into a unit fault discrimination model, obtaining fault types output by the unit fault discrimination model and feeding back the fault types to a control background, wherein the fault types comprise overload, blockage, water leakage and refrigerant shortage.
An electronic device comprises a power supply, an interface, a keyboard, a memory, a central processing unit and a computer program stored in the memory and capable of running on the central processing unit, wherein the remote monitoring management method of the intelligent BA automatic control system of the new energy source is realized when the computer program is executed by the processor, the interface comprises a network interface and a data interface, the network interface comprises a wired or wireless interface, and the data interface comprises an input or output interface.
A computer readable medium having stored thereon a computer program which when executed implements the above-described remote monitoring management method of the new energy intelligent factory BA automation system.
Compared with the prior art, the invention has the beneficial effects that:
according to the remote monitoring management method of the intelligent BA automatic control system of the new energy factory, only the real-time temperature value of each sub-area is needed to be detected, the target sub-area is determined according to the accurate temperature value or the temperature influence coefficient in the follow-up process, and then the air conditioner units with faults are searched from the target sub-area, so that the temperature control of each sub-area can be completed without monitoring each air conditioner unit in real time, the operation load of the intelligent factory management system is reduced, and the intelligent factory management system is ensured to be capable of operating stably.
Drawings
FIG. 1 is a schematic diagram of a remote monitoring management method of a BA automatic control system of a new energy intelligent factory;
FIG. 2 is a schematic diagram of a remote monitoring management system of the BA automatic control system of the new energy intelligent factory;
FIG. 3 is a schematic diagram of an electronic device;
fig. 4 is a schematic diagram of a computer-readable storage medium.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
As shown in fig. 1, the present embodiment provides a remote monitoring management method for a BA automatic control system of a new energy smart factory, which includes:
s10: dividing intelligent factory areas, acquiring real-time temperature values of each sub-area, and marking, wherein the intelligent factory areas are factory building internal areas;
it should be noted that, the smart factory area refers to a single factory internal area, and then the factory internal area can be divided according to different functions, for example, a common factory internal area can be divided into a production area, a storage area, an office area, a living area and an environment-friendly area, and then the production area, the storage area, the office area, the living area and the environment-friendly area are subareas in the embodiment, the production area is used as a core production area of the factory, the production area needs to have a spacious space and good ventilation, the requirement on the area temperature is strict, the office area is an important area for enterprise management, including an administrative office and a meeting room, the living area is a living area of staff, including a canteen, an activity room and the like, the requirement on the temperature of the office area and the living area is completely different from that of the production area, and the requirement on the air conditioning unit of each subarea is regulated and controlled by the air conditioning unit of each subarea, so the requirement on the air conditioning unit of each subarea is different;
s20: acquiring an external temperature change value, the number of people in each sub-area and the corresponding sub-area, and judging to switch to S30 or S40 according to the sub-area, wherein the external temperature change value is a temperature difference value of the factory building in unit time;
specifically, the temperature outside the factory area has a significant influence on the interior of the factory area, the temperature change of the external environment directly affects the environment temperature inside the factory area, for example, the temperature inside the factory area is possibly increased due to the excessively high external temperature, so that the normal production is affected, influence factors outside the factory area are required to be eliminated, meanwhile, the influence of the number of people on the indoor temperature exists, on one hand, the human body is used as a heat source, the number of the human body directly affects the indoor temperature, the human body can generate heat through breathing, sweat evaporation and other modes, when the number of people increases, the generation of the heat also correspondingly increases, and thus the indoor temperature is possibly increased, and on the other hand, the change of the number of people also affects the indoor ventilation and air flow conditions;
the personnel number acquisition method of each sub-area comprises the following steps: the entrance guard management system is adopted to count the number of people and track the positions of operators, so that the automatic entry and exit records of the operators are realized, and the number of the operators is counted accurately in real time;
and (3) judging to switch to S30 or S40 according to the subarea area, wherein the specific logic is as follows:
when the area of the sub-area is larger than the area of the preset area, the step S30 is carried out;
when the area of the sub-area is smaller than or equal to the area of the preset area, the step S40 is performed;
it will be appreciated that when the area of the area is larger, the number of people in the area is not the most important factor affecting the temperature of the area, for example, a common factory building interior area can be divided into a production area, a storage area, an office area, a living area and an environmental protection area, the areas of the production area and the storage area are far larger than those of the office area, so that the temperatures in the production area and the storage area can not change obviously due to the increase of the number of people, but the office area is different and mainly comprises an administrative office and a meeting room, and the temperatures in the office area can change obviously due to the increase of the number of people.
S30: inputting the real-time temperature value and the external temperature change value into an internal temperature correction model, obtaining an internal accurate temperature value output by the internal temperature correction model, and turning to S50;
specifically, in some areas, for example, in a Xinjiang area, the day-night temperature difference can reach more than 25 degrees, in this embodiment, the temperature control on each sub-area can be completed mainly by detecting the real-time temperature value of each sub-area, and according to the subsequent further confirmation work, the influence of the temperature outside the factory area on the interior of the factory area is required to be eliminated, so that the temperature monitoring on each sub-area is more accurate;
the training process of the internal temperature correction model is as follows: obtaining a sample data set, wherein the sample data set comprises a real-time temperature value, an external temperature change value and an internal accurate temperature value, dividing the sample data set into a sample training set and a sample test set, constructing a regression network, taking the real-time temperature value and the external temperature change value in the sample training set as input data of the regression network, taking the internal accurate temperature value in the sample training set as output data of the regression network, training the regression network to obtain an initial regression network for predicting the internal accurate temperature value, testing the initial regression network by utilizing the sample test set, and outputting the regression network meeting the preset test accuracy as an internal temperature correction model, wherein the regression network is specifically one of a decision tree regression model, a linear regression model and a neural network model;
it should be noted that, the internal accurate temperature value is obtained by the following steps: under the experimental environment, the internal region of the factory building is in a emptying state, under the condition of external temperature change, a first real-time temperature value of the internal region of the factory building is measured, a real-time temperature difference value is generated according to the first real-time temperature value, the real-time temperature difference value is a change value of real-time temperature in unit time, the internal region of the factory building is in a conventional state, the conventional state means that a machine of the internal region of the factory building works normally, then under the condition of approximately the same external temperature change, a second real-time temperature value of the internal region of the factory building is measured, and the internal accurate temperature value is the second real-time temperature value minus the real-time temperature difference value;
s40: generating a temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number, and switching to S50;
in this embodiment, the method for generating the temperature influence coefficient according to the real-time temperature value, the external temperature change value and the number of people includes:
；
in the method, in the process of the invention,is->Temperature influence coefficient of sub-region, +.>Is->Real-time temperature values for the individual sub-zones,is->Standard temperature value of sub-region, +.>Is->External temperature variation value of sub-region, +.>Is->Number of people in sub-area->、/>And->Are all weight factors;
wherein,for the standard temperature value, the standard temperature value refers to the best set temperature value in each sub-area, since each sub-area is toThe standard temperature values of each sub-area are different when the temperature requirements are different, and the standard temperature values are selected to be in one-to-one correspondence with the marks of the real-time temperature values;
it can be understood that when the real-time temperature value deviates from the standard temperature value, the temperature influence coefficient is larger, the serious unbalance of the temperature in the subarea is indicated, and meanwhile, when the external temperature change value is larger or the number of personnel is larger, the temperature influence coefficient is smaller, the phenomenon that the unbalance of the temperature is indicated to be the influence of external factors or personnel aggregation, the probability of representing the damage of the air conditioning units in the subarea is smaller, and the follow-up investigation of the air conditioning units in the subarea is not needed;
s50: determining a target subregion based on the internal accurate temperature value or the temperature influence coefficient;
logic for determining a target sub-region based on an internal accurate temperature value includes:
when the internal accurate temperature value is larger than or equal to a first preset internal temperature threshold value and smaller than or equal to a second preset internal temperature threshold value, marking a subarea corresponding to the internal accurate temperature value as a target subarea, wherein the second preset internal temperature threshold value is larger than the first preset internal temperature threshold value;
when the internal accurate temperature value is smaller than a first preset internal temperature threshold value or larger than a second preset internal temperature threshold value, marking a subarea corresponding to the internal accurate temperature value as a target subarea;
it can be understood that each sub-area has a corresponding temperature range interval, if the internal accurate temperature value of the sub-area is not in the temperature range interval, the internal temperature of the sub-area is unbalanced, and the phenomenon that the temperature is unbalanced is caused by the failure of the air conditioning unit in the sub-area is very probable, so that the air conditioning unit in the sub-area needs to be examined;
the logic for determining the target sub-region based on the temperature influence coefficient comprises:
when the temperature influence coefficient is larger than or equal to a preset influence coefficient, marking a subarea corresponding to the internal accurate temperature value as a target subarea;
when the temperature influence coefficient is smaller than the preset influence coefficient, the subarea corresponding to the temperature influence coefficient is not marked as a target subarea.
S60: acquiring working state data of all air conditioning units in a target subarea, and converting the working state data into standard state data according to a preset data conversion method, wherein the working state data comprise fan frequency, return air temperature, water valve frequency and filter screen pressure difference;
it should be noted that, because the area of the subarea is too large, the number of air conditioning units needed by the subarea is large, and the specifications and brands of the air conditioning units are different, the working state data need to be standardized to accurately judge the working state of the air conditioning units, and it is noted that the embodiment only converts the format of the working state data, but does not change the actual numerical value;
the return air temperature is the air temperature in a return air pipeline of the air conditioning unit and is used for controlling the refrigerating or heating effect of the air conditioning unit, the water valve frequency is the switching frequency of a water valve in the air conditioning unit and is used for controlling the water flow in the air conditioning unit, the filter screen pressure difference refers to the pressure difference between the front and the rear of the filter and is used for monitoring whether the filter needs to be replaced or cleaned;
the data conversion method specifically comprises the following steps:
s601: determining the data type and the data unit of the working state data;
s602: judging whether the data unit is a standard unit, if so, replacing the working state data with the standard state data for output, and if not, turning to S603;
s603: converting the data unit into a standard unit through a unit conversion algorithm, and replacing the working state data with the standard state data for output;
it can be understood that the data types include temperature, frequency or pressure difference, etc., and the data units include temperature, such as celsius temperature, fahrenheit temperature, kelvin, etc., and the standard units can be preset by an experimenter, for example, the celsius temperature is set as the standard unit, then the unit of the return air temperature is celsius temperature, then the return air temperature is directly output, if the unit of the return air temperature is fahrenheit temperature, the return air temperature is converted into celsius temperature and then output, so that the working state of the air conditioning unit can be conveniently judged later, and the unit conversion algorithm in the above is the prior art.
S70: inputting standard state data into a unit fault discrimination model, obtaining fault categories output by the unit fault discrimination model and feeding the fault categories back to a control background, wherein the fault categories comprise overload, blockage, water leakage and refrigerant deficiency;
specifically, the training process of the unit fault discrimination model is as follows: obtaining multiple groups of data, wherein the data comprise standard state data and fault categories, the standard state data and the fault categories are used as sample sets, the sample sets are divided into training sets and test sets, a classifier is constructed, the standard state data in the training sets are used as input data, the fault categories in the training sets are used as output data, the classifier is trained to obtain an initial classifier, the test sets are used for testing the initial classifier, the classifier meeting the preset accuracy is output to serve as a unit fault judging model, and the classifier is preferably a convolutional neural network model or a naive Bayesian model;
according to the remote monitoring management method of the intelligent BA automatic control system of the new energy factory, only the real-time temperature value of each sub-area is needed to be detected, the target sub-area is determined according to the accurate temperature value or the temperature influence coefficient in the subsequent process, and then the air conditioner units with faults are searched from the target sub-area, so that the temperature control of each sub-area can be completed without monitoring each air conditioner unit in real time, the operation load of the intelligent factory management system is reduced, and the intelligent factory management system can be ensured to operate stably;
example 2
As shown in fig. 2, based on embodiment 1, the present embodiment discloses a remote monitoring management system of a new energy smart factory BA automatic control system, comprising:
and a data acquisition module: dividing intelligent factory areas, acquiring real-time temperature values of each sub-area, and marking, wherein the intelligent factory areas are factory building internal areas;
and a judging module: acquiring an external temperature change value, the number of people in each sub-area and the corresponding sub-area, and judging whether to switch to a temperature correction module or a coefficient generation module according to the sub-area, wherein the external temperature change value is a temperature difference value of the factory building in unit time;
the personnel number acquisition method of each sub-area comprises the following steps: the entrance guard management system is adopted to count the number of people and track the positions of operators, so that the automatic entry and exit records of the operators are realized, and the number of the operators is counted accurately in real time;
the logic for judging whether to switch to the temperature correction module or the coefficient generation module according to the subarea area is as follows:
when the area of the sub-area is larger than the area of the preset area, transferring to a temperature correction module;
when the area of the sub-area is smaller than or equal to the area of the preset area, transferring to a coefficient generation module;
it can be understood that when the area of the area is larger, the number of people in the area is not the most important factor affecting the temperature of the area, for example, a common factory building interior area can be divided into a production area, a storage area, an office area, a living area and an environment-friendly area, the areas of the production area and the storage area are far larger than those of the office area, so that the temperatures in the production area and the storage area can not obviously change due to the increase of the number of people, but the office area is different and mainly comprises an administrative office and a meeting room, and the temperatures in the office area can obviously change due to the increase of the number of people, so that whether the number of people is regarded as the main factor affecting the real-time temperature value is confirmed through the area of the subarea in the embodiment, and the accuracy of judging whether the temperature abnormality occurs in the area can be improved;
and a temperature correction module: inputting the real-time temperature value and the external temperature change value into an internal temperature correction model, obtaining an internal accurate temperature value output by the internal temperature correction model, and transferring to a region determining module;
and a coefficient generation module: generating a temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number, and transferring to a region determining module;
the method for generating the temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number comprises the following steps:
；
in the method, in the process of the invention,is->Temperature influence coefficient of sub-region, +.>Is->Real-time temperature values for the individual sub-zones,is->Standard temperature value of sub-region, +.>Is->External temperature variation value of sub-region, +.>Is->Number of people in sub-area->、/>And->Are all weight factors;
wherein,the standard temperature value is the optimal set temperature value in each sub-area, and the standard temperature value of each sub-area is different because the requirements of each sub-area on the temperature are different, so that the standard temperature value is selected to be in one-to-one correspondence with the mark of the real-time temperature value;
the area determination module: determining a target subregion based on the internal accurate temperature value or the temperature influence coefficient;
and a conversion module: acquiring working state data of all air conditioning units in a target subarea, and converting the working state data into standard state data according to a preset data conversion method, wherein the working state data comprise fan frequency, return air temperature, water valve frequency and filter screen pressure difference;
and a feedback module: and inputting the standard state data into a unit fault discrimination model, obtaining fault types output by the unit fault discrimination model and feeding back the fault types to a control background, wherein the fault types comprise overload, blockage, water leakage and refrigerant shortage.
Example 3
As shown in fig. 3, the disclosure of this embodiment provides an electronic device, which includes a power source, an interface, a keyboard, a memory, a central processing unit, and a computer program stored on the memory and capable of running on the central processing unit, where the central processing unit implements any one of the methods provided by the foregoing methods when executing the computer program, and the interface includes a network interface and a data interface, where the network interface includes a wired or wireless interface, and the data interface includes an input or output interface.
Example 4
As shown in fig. 4, the disclosure of the present embodiment provides a computer readable medium having a computer program stored thereon, where the computer program is executed to implement the remote monitoring management method of the new energy intelligent factory BA automatic control system provided by the above methods.
The above formulas are all formulas with dimensionality removed and numerical value calculated, the formulas are formulas with the latest real situation obtained by software simulation by collecting a large amount of data, and preset parameters, weights and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The remote monitoring management method of the intelligent BA automatic control system of the new energy factory is characterized by comprising the following steps of:
s10: dividing intelligent factory areas, acquiring real-time temperature values of each sub-area, and marking, wherein the intelligent factory areas are factory building internal areas;
s20: acquiring an external temperature change value, the number of people in each sub-area and the corresponding sub-area, and judging to switch to S30 or S40 according to the sub-area, wherein the external temperature change value is a temperature difference value of the factory building in unit time;
the logic for judging to switch to S30 or S40 according to the subarea area is as follows:
when the area of the sub-area is larger than the area of the preset area, the step S30 is carried out;
when the area of the sub-area is smaller than or equal to the area of the preset area, the step S40 is performed;
s30: inputting the real-time temperature value and the external temperature change value into an internal temperature correction model, obtaining an internal accurate temperature value output by the internal temperature correction model, and turning to S50;
s40: generating a temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number, and switching to S50;
the method for generating the temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number comprises the following steps:
；
in the method, in the process of the invention,is->Temperature influence coefficient of sub-region, +.>Is->Real-time temperature values of individual sub-areas, +.>Is->Standard temperature value of sub-region, +.>Is->External temperature variation value of sub-region, +.>Is->Number of people in sub-area->、/>And->Are all weight factors;
s50: determining a target subregion based on the internal accurate temperature value or the temperature influence coefficient;
s60: acquiring working state data of all air conditioning units in a target subarea, and converting the working state data into standard state data according to a preset data conversion method, wherein the working state data comprise fan frequency, return air temperature, water valve frequency and filter screen pressure difference;
s70: and inputting the standard state data into a unit fault discrimination model, obtaining fault types output by the unit fault discrimination model and feeding back the fault types to a control background, wherein the fault types comprise overload, blockage, water leakage and refrigerant shortage.
2. The remote monitoring and managing method of the intelligent new energy factory BA automatic control system according to claim 1, wherein the personnel number obtaining method of each sub-area is as follows: the entrance guard management system is used for counting the number of people and tracking the positions of operators, so that the automatic record of personnel entering and exiting is realized, and the number of the operators is counted accurately in real time.
3. The remote monitoring management method of the intelligent new energy factory BA automatic control system according to claim 1, wherein the training process of the internal temperature correction model is: the method comprises the steps of obtaining a sample data set, wherein the sample data set comprises a real-time temperature value, an external temperature change value and an internal accurate temperature value, dividing the sample data set into a sample training set and a sample testing set, constructing a regression network, taking the real-time temperature value and the external temperature change value in the sample training set as input data of the regression network, taking the internal accurate temperature value in the sample training set as output data of the regression network, training the regression network to obtain an initial regression network for predicting the internal accurate temperature value, testing the initial regression network by utilizing the sample testing set, and outputting the regression network meeting the preset test accuracy as an internal temperature correction model.
4. The method for remote monitoring and management of a new energy intelligent factory BA automation system according to claim 1, wherein the logic for determining the target sub-area based on the internal accurate temperature value comprises:
when the internal accurate temperature value is larger than or equal to a first preset internal temperature threshold value and smaller than or equal to a second preset internal temperature threshold value, marking the subarea corresponding to the internal accurate temperature value as a target subarea, wherein the second preset internal temperature threshold value is larger than the first preset internal temperature threshold value;
when the internal accurate temperature value is smaller than the first preset internal temperature threshold value or larger than the second preset internal temperature threshold value, marking the subarea corresponding to the internal accurate temperature value as a target subarea.
5. The method for remote monitoring and management of a new energy intelligent factory BA automation system according to claim 1, wherein the logic for determining the target subregion based on the temperature influence coefficient comprises:
when the temperature influence coefficient is larger than or equal to a preset influence coefficient, marking a subarea corresponding to the internal accurate temperature value as a target subarea;
when the temperature influence coefficient is smaller than the preset influence coefficient, the subarea corresponding to the temperature influence coefficient is not marked as a target subarea.
6. The remote monitoring management method of the intelligent new energy factory BA automatic control system according to claim 1, wherein said data conversion method comprises:
s601: determining the data type and the data unit of the working state data;
s602: judging whether the data unit is a standard unit, if so, replacing the working state data with the standard state data for output, and if not, turning to S603;
s603: and converting the data unit into a standard unit through a unit conversion algorithm, and replacing the working state data with the standard state data for output.
7. The remote monitoring and managing method of the intelligent new energy factory BA automatic control system according to claim 6, wherein the training process of the unit fault discrimination model is: obtaining multiple groups of data, wherein the data comprise standard state data and fault categories, taking the standard state data and the fault categories as sample sets, dividing the sample sets into training sets and test sets, constructing a classifier, taking the standard state data in the training sets as input data, taking the fault categories in the training sets as output data, training the classifier to obtain an initial classifier, testing the initial classifier by using the test sets, and outputting the classifier meeting the preset accuracy as a unit fault judging model.
8. A remote monitoring management system of a new energy intelligent factory BA automatic control system for implementing the new energy intelligent factory BA automatic control system remote monitoring management method according to any one of claims 1 to 7, characterized by comprising:
and a data acquisition module: dividing intelligent factory areas, acquiring real-time temperature values of each sub-area, and marking, wherein the intelligent factory areas are factory building internal areas;
and a judging module: acquiring an external temperature change value, the number of people in each sub-area and the corresponding sub-area, and judging whether to switch to a temperature correction module or a coefficient generation module according to the sub-area, wherein the external temperature change value is a temperature difference value of the factory building in unit time;
the logic for judging whether to switch to the temperature correction module or the coefficient generation module according to the subarea area is as follows:
when the area of the sub-area is larger than the area of the preset area, transferring to a temperature correction module;
when the area of the sub-area is smaller than or equal to the area of the preset area, transferring to a coefficient generation module;
and a temperature correction module: inputting the real-time temperature value and the external temperature change value into an internal temperature correction model, obtaining an internal accurate temperature value output by the internal temperature correction model, and transferring to a region determining module;
and a coefficient generation module: generating a temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number, and transferring to a region determining module;
the method for generating the temperature influence coefficient according to the real-time temperature value, the external temperature change value and the personnel number comprises the following steps:
；
in the method, in the process of the invention,is->Temperature influence coefficient of sub-region, +.>Is->Real-time temperature values of individual sub-areas, +.>Is->Standard temperature value of sub-region, +.>Is->External temperature variation value of sub-region, +.>Is->Number of people in sub-area->、/>And->Are all weight factors;
the area determination module: determining a target subregion based on the internal accurate temperature value or the temperature influence coefficient;
and a conversion module: acquiring working state data of all air conditioning units in a target subarea, and converting the working state data into standard state data according to a preset data conversion method, wherein the working state data comprise fan frequency, return air temperature, water valve frequency and filter screen pressure difference;
and a feedback module: and inputting the standard state data into a unit fault discrimination model, obtaining fault types output by the unit fault discrimination model and feeding back the fault types to a control background, wherein the fault types comprise overload, blockage, water leakage and refrigerant shortage.
9. An electronic device comprising a power supply, an interface, a keyboard, a memory, a central processing unit and a computer program stored on the memory and operable on the central processing unit, wherein the central processing unit implements the remote monitoring management method of the intelligent new energy factory BA autonomous system according to any one of claims 1 to 7 when executing the computer program, the interface comprises a network interface and a data interface, the network interface comprises a wired or wireless interface, and the data interface comprises an input or output interface.
10. A computer readable medium having stored thereon a computer program, wherein the computer program when executed implements the remote monitoring management method of the new energy intelligent factory BA automation system according to any one of claims 1 to 7.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005133980A (en) * | 2003-10-28 | 2005-05-26 | Mitsubishi Electric Corp | Air conditioning system |
| WO2012090718A1 (en) * | 2010-12-28 | 2012-07-05 | インターナショナル・ビジネス・マシーンズ・コーポレーション | Method, computer program, and computer for determining status of system |
| CN112394697A (en) * | 2020-11-24 | 2021-02-23 | 中国铁路设计集团有限公司 | Railway station building equipment monitoring and energy management system, program and storage medium |
| CN112584100A (en) * | 2020-12-04 | 2021-03-30 | 南京乐之飞科技有限公司 | Exhibition hall intelligent management and control platform based on personnel dynamic distribution tracking analysis |
| CN115095907A (en) * | 2022-07-15 | 2022-09-23 | 唐山学院 | Intelligent heat supply energy-saving regulation and control method and system based on deep learning and storage medium |
| CN115309203A (en) * | 2022-10-11 | 2022-11-08 | 山东华邦农牧机械股份有限公司 | Intelligent temperature control system for cultivation |
| CN115375264A (en) * | 2022-08-08 | 2022-11-22 | 江苏安科瑞微电网研究院有限公司 | Intelligent park comprehensive management system and management method thereof |
| CN116184948A (en) * | 2022-12-19 | 2023-05-30 | 北京市市政工程设计研究总院有限公司 | Intelligent monitoring disc for water plant and application system and method of early warning diagnosis technology |
| WO2023169519A1 (en) * | 2022-03-11 | 2023-09-14 | 青岛海信日立空调系统有限公司 | Multi-split air conditioning system and fault-tolerant control method therefor |
-
2024
- 2024-01-19 CN CN202410078752.3A patent/CN117608255B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005133980A (en) * | 2003-10-28 | 2005-05-26 | Mitsubishi Electric Corp | Air conditioning system |
| WO2012090718A1 (en) * | 2010-12-28 | 2012-07-05 | インターナショナル・ビジネス・マシーンズ・コーポレーション | Method, computer program, and computer for determining status of system |
| CN112394697A (en) * | 2020-11-24 | 2021-02-23 | 中国铁路设计集团有限公司 | Railway station building equipment monitoring and energy management system, program and storage medium |
| CN112584100A (en) * | 2020-12-04 | 2021-03-30 | 南京乐之飞科技有限公司 | Exhibition hall intelligent management and control platform based on personnel dynamic distribution tracking analysis |
| WO2023169519A1 (en) * | 2022-03-11 | 2023-09-14 | 青岛海信日立空调系统有限公司 | Multi-split air conditioning system and fault-tolerant control method therefor |
| CN115095907A (en) * | 2022-07-15 | 2022-09-23 | 唐山学院 | Intelligent heat supply energy-saving regulation and control method and system based on deep learning and storage medium |
| CN115375264A (en) * | 2022-08-08 | 2022-11-22 | 江苏安科瑞微电网研究院有限公司 | Intelligent park comprehensive management system and management method thereof |
| CN115309203A (en) * | 2022-10-11 | 2022-11-08 | 山东华邦农牧机械股份有限公司 | Intelligent temperature control system for cultivation |
| CN116184948A (en) * | 2022-12-19 | 2023-05-30 | 北京市市政工程设计研究总院有限公司 | Intelligent monitoring disc for water plant and application system and method of early warning diagnosis technology |
Non-Patent Citations (2)
| Title |
|---|
| 地铁智慧车站的架构和系统建设――以青岛地铁为背景;陈思;;中阿科技论坛(中英阿文);20200710(07);全文 * |
| 陈思 ; .地铁智慧车站的架构和系统建设――以青岛地铁为背景.中阿科技论坛(中英阿文).2020,(07),全文. * |
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