CN112947158B - Building intelligent electrical control system - Google Patents
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Abstract
The invention discloses a building intelligent electrical control system, which utilizes a building acquisition unit to acquire data information of a building, and utilizes an electrical acquisition unit to acquire electrical information of the building; positioning and processing the coordinates of the floors and the building households on the building by using a data positioning module to obtain coordinate processing information; the data analysis module is used for receiving the data information and the electrical information, analyzing the data information to obtain data analysis information, analyzing the electrical information to obtain electrical analysis information, and combining the data analysis information and the electrical analysis information to obtain a data analysis set; the data processing module is used for receiving and processing the data analysis set to obtain a data processing set, and the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set; the invention is used for solving the problems that the electrical operation state of each building in a building cannot be monitored and the building in an abnormal state is early warned and regulated.
Description
Technical Field
The invention relates to the technical field of intelligent electrical control, in particular to a building intelligent electrical control system.
Background
The electrical control system is generally called an electrical equipment secondary control circuit, different equipment has different control circuits, and the control modes of high-voltage electrical equipment and low-voltage electrical equipment are different. Specifically, the electrical control system is a combination of a plurality of electrical elements, and is used for controlling a certain object or objects, so as to ensure that a controlled device safely and reliably operates, and the main functions of the electrical control system are automatic control, protection, monitoring and measurement.
Publication No. CN105068438A discloses an intelligent electric appliance control system and method for large-scale office buildings, which comprises an intelligent electric and energy optimization monitoring center, wherein the intelligent electric and energy optimization monitoring center is communicated with a wireless router through a network, the energy optimization monitoring center is communicated with a data server through the network, the wireless router is communicated with floor gateways, the router is communicated with the floor gateways, and the floor gateways are communicated with a plurality of monitoring centers inside the buildings in a wireless mode; the system has the characteristics of high precision, low cost, simple structure, low power consumption and the like, is added with an optimized program, and has very high practicability.
The existing intelligent electrical control system for buildings has the following defects: the problem that the electrical operation state of each building in the building cannot be monitored, and the problem that the building in an abnormal state is early warned and regulated is solved.
Disclosure of Invention
The invention aims to provide an intelligent electrical control system for a building, and the technical problem to be solved by the invention is as follows:
how to solve the problem that can not monitor the electrical operation state of every building family in the building among the current scheme to and the problem of early warning and regulation and control to the building family that appears abnormal state.
The purpose of the invention can be realized by the following technical scheme: an intelligent electrical control system for a building comprises a data acquisition module, a data positioning module, a data analysis module, a data processing module and an early warning control module;
the data acquisition module comprises a building acquisition unit and an electrical acquisition unit, the building acquisition unit is used for acquiring data information of a building, the data information comprises floor data, building data and accommodation data, the electrical acquisition unit is used for acquiring electrical information of the building, the electrical information comprises equipment data, power data and electricity charge data, and the data information and the electrical information are sent to the data analysis module;
the data positioning module is used for positioning and processing the coordinates of floors and households on the building to obtain coordinate processing information and sending the coordinate processing information to the data analysis module;
the data analysis module is used for receiving the data information and the electrical information, analyzing the data information to obtain data analysis information, analyzing the electrical information to obtain electrical analysis information, combining the data analysis information and the electrical analysis information to obtain a data analysis set, and sending the data analysis set to the data processing module;
the data processing module is used for receiving and processing the data analysis set to obtain a data processing set, and the specific steps comprise:
the method comprises the following steps: receiving data analysis information and electrical analysis information in the data analysis set;
step two: acquiring a bearing value in the data analysis information and an actual operation value in the electrical analysis information;
step three: calculating and acquiring a real span coefficient of a bearing value and a real operation value by using a formula, wherein the formula is as follows:
wherein Q isskExpressed as the real span coefficient, QczExpressed as a load-bearing value, QsyThe actual operation value is represented, eta is represented as a preset actual span correction factor, and c1 and c2 are represented as different proportionality coefficients;
step four: matching the real span coefficient with a preset standard real span threshold value, and if the real span coefficient is not greater than the standard real span threshold value, judging that the building electrical state corresponding to the real span coefficient normally operates and generating a first matching signal; if the real span coefficient is larger than the standard real span threshold value, judging that the building electrical operation state corresponding to the real span coefficient is abnormal and generating a second matching signal;
step five: marking the building corresponding to the real span coefficient as a building to be tested according to the second matching signal, marking the electric appliance in the building to be tested as an electric appliance to be tested, and marking the real-time temperature mean value in the building to be tested as a temperature mean value to be tested;
step six: classifying and combining the first matching signal, the second matching signal, the marked building to be tested, the electric appliance to be tested and the temperature mean value to be tested to obtain a data processing set;
and the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set.
Preferably, the data analysis information is obtained by analyzing the data information, and the specific steps include:
s21: acquiring floor data, building data and accommodation data in the data information;
s22: marking a plurality of floors in floor data from bottom to top to obtain a floor mark set LBi, wherein i is 1,2.. n; acquiring building users of a plurality of floors according to building data, and marking the building users from left to right to obtain a building mark set FBi, wherein i is 1,2.. n;
s23: acquiring the number of resident people and resident groups in each building in the lodging data according to the building data, setting the resident groups in different age groups to correspond to different class preset values, matching the resident groups in each building with all the resident groups according to the age groups to acquire the corresponding class preset values, and marking the corresponding class preset values as JYi, wherein i is 1,2.
S24: acquiring real-time average indoor temperature in the lodging data and marking as SWi, i is 1,2.. n;
s25: normalizing the marked household preset value and the real-time average temperature, taking values, and calculating by using a formula to obtain the bearing value of the building, wherein the formula is as follows:
wherein Q isczExpressed as a load value, mu is expressed as a preset load correction factor, a1, a2 and a3 are expressed as different proportionality coefficients, and JR is expressed as the number of residents in each building;
s26: and classifying and combining the bearing value and the number of the resident people with the marked floor mark set, the living class preset value and the real-time average temperature to obtain data analysis information.
Preferably, the electrical information is analyzed to obtain electrical analysis information, and the specific steps include:
s31: acquiring equipment data, power data and electric charge data in the electrical information;
s32: setting different equipment types to correspond to different equipment preset values, matching the equipment types in the equipment data with all the equipment types to obtain corresponding equipment preset values, and marking the equipment preset values as SYi, wherein i is 1,2.. n; acquiring standard power corresponding to different device types in device data and marking the standard power as SBGi, i is 1,2.. n;
s33: counting the standard total power of each household device and marking as BZGi, i is 1,2.. n; acquiring the actual total power of each household device in the power data and marking as SZGi, i is 1,2.. n;
s34: acquiring an electric charge value of each household every day in the electric charge data and a total power value and a temperature mean value corresponding to the electric charge value, setting different temperature values to correspond to different preset temperature values, matching the indoor temperature mean value of each household every day with all the temperature values to acquire the corresponding preset temperature value, and marking the preset temperature value as WYi, i is 1,2. Marking the electricity charge value as DFi, i ═ 1,2.. n; marking a total power value corresponding to the electric charge value as DGZi, i-1, 2.. n;
s35: normalizing the marked equipment preset value, standard power, standard total power, actual total power, temperature preset value, electric charge value and total power value and taking values;
s36: the real operation value of the electrical information is obtained by using a formula, wherein the formula is as follows:
wherein Q issyExpressed as actual running values, b1, b2 and b3 are expressed as different proportionality coefficients;
s37: and classifying and combining the actual operation value and the marked equipment preset value, standard power, standard total power, actual total power, temperature preset value, electric charge value and total power value to obtain electrical analysis information.
Preferably, the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set, and the specific steps include:
s41: receiving and analyzing a data processing set;
s42: if the data processing set contains a second matching signal, acquiring a building to be tested, an electric appliance to be tested and a temperature average value to be tested corresponding to the second matching signal;
s43: acquiring real-time power of an electric appliance to be tested and standard power corresponding to the electric appliance to be tested, respectively marking the real-time power and the standard power as D1 and D2, acquiring body surface temperature of the electric appliance to be tested and marking the body surface temperature as D3, and marking the mean value of the temperature to be tested as D4;
s44: calculating the positive and negative values of each electrical appliance to be measured by using a formula, wherein the formula is as follows:
wherein Q iszyExpressing as positive and different values, expressing as preset positive and different correction factors, and expressing as different proportionality coefficients g1, g2 and g 3;
s45: acquiring a standard normal and abnormal range preset by an electric appliance to be tested, matching the normal and abnormal value with the standard normal and abnormal range, and if the normal and abnormal value is smaller than the minimum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a first early warning signal; if the normal and abnormal values belong to the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is normal and generating a forecast signal; if the normal and abnormal values are larger than the maximum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a second early warning signal;
s46: and early warning is carried out on the building to be detected according to the first early warning signal and the second early warning signal.
Preferably, early warning is carried out to the building family that awaits measuring according to first early warning signal and second early warning signal, and specific step includes:
s51: acquiring a network address of a building to be detected corresponding to the first early warning signal and the second early warning signal, marking the network address as a communication address, acquiring connection equipment of the communication address, and sending the first early warning signal and the second early warning signal to the connection equipment by using the communication address for prompting and generating a prompting signal;
s52: if the to-be-detected electrical appliance corresponding to the first early warning signal and the second early warning signal stops running within a preset time period after the prompt signal is generated, judging that the user receives the prompt signal and processing the prompt signal;
s53: and if the running states of the electric appliances to be tested corresponding to the first early warning signal and the second early warning signal are unchanged within a preset time period after the prompt signal is generated, judging that the prompt signal received by the user is abnormal and not processed, and performing power-off processing on the building to be tested according to the prompt signal.
The invention has the beneficial effects that:
in each aspect disclosed by the invention, the data acquisition module, the data positioning module, the data analysis module, the data processing module and the early warning control module are used in a matched manner, so that the purposes of monitoring the electrical operation state of each building in a building and early warning and regulating the building in an abnormal state can be achieved;
the data acquisition module comprises a building acquisition unit and an electrical acquisition unit, the building acquisition unit is used for acquiring data information of a building, the data information comprises floor data, building data and accommodation data, the electrical acquisition unit is used for acquiring electrical information of the building, the electrical information comprises equipment data, power data and electricity charge data, and the data information and the electrical information are sent to the data analysis module; by collecting data information of the building and electrical information of the building, effective data support can be provided for monitoring, early warning, regulation and control of electrical operating states of the building;
the data positioning module is used for positioning and processing the coordinates of the floors and the building households on the building to obtain coordinate processing information, and the coordinate processing information is sent to the data analysis module; by positioning and processing the coordinates of the floors and the building families on the building, the positions of the building families can be quickly obtained, and quick positioning, early warning and regulation and control can be carried out in an abnormal state;
the data analysis module is used for receiving the data information and the electrical information, analyzing the data information to obtain data analysis information, analyzing the electrical information to obtain electrical analysis information, combining the data analysis information and the electrical analysis information to obtain a data analysis set, and sending the data analysis set to the data processing module; the bearing value and the actual operation value are obtained by calculating the data information and the electrical information, so that the relation is established between each data item in the data information and the electrical information, and the accuracy and the relevance of the data can be improved;
the data processing module is used for receiving and processing the data analysis set to obtain a data processing set, and the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set; the real span coefficient is obtained by processing and calculating the data analysis set, the real span coefficient is analyzed to obtain the running state of a building family, early warning and regulation are timely carried out, fire hazard elimination potential safety hazards are prevented from happening, and the intelligence of electrical control is improved.
Drawings
The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a block diagram of an intelligent electrical control system for a building according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention relates to an intelligent electrical control system for a building, which comprises a data acquisition module, a data positioning module, a data analysis module, a data processing module and an early warning control module;
the data acquisition module comprises a building acquisition unit and an electrical acquisition unit, the building acquisition unit is used for acquiring data information of a building, the data information comprises floor data, building data and accommodation data, the electrical acquisition unit is used for acquiring electrical information of the building, the electrical information comprises equipment data, power data and electricity charge data, and the data information and the electrical information are sent to the data analysis module;
the data positioning module is used for positioning and processing the coordinates of floors and households on the building to obtain coordinate processing information and sending the coordinate processing information to the data analysis module;
the data analysis module is used for receiving data information and electrical information, analyzing the data information and obtaining data analysis information, and the specific steps comprise:
acquiring floor data, building data and accommodation data in the data information;
marking a plurality of floors in floor data from bottom to top to obtain a floor mark set LBi, wherein i is 1,2.. n; acquiring building users of a plurality of floors according to building data, and marking the building users from left to right to obtain a building mark set FBi, wherein i is 1,2.. n;
acquiring the number of resident people and resident groups in each building in the lodging data according to the building data, setting the resident groups in different age groups to correspond to different class preset values, matching the resident groups in each building with all the resident groups according to the age groups to acquire the corresponding class preset values, and marking the corresponding class preset values as JYi, wherein i is 1,2.
Acquiring real-time average indoor temperature in the lodging data and marking as SWi, i is 1,2.. n;
normalizing the marked household preset value and the real-time average temperature, taking values, and calculating by using a formula to obtain the bearing value of the building, wherein the formula is as follows:
wherein Q isczExpressed as a load value, mu is expressed as a preset load correction factor, a1, a2 and a3 are expressed as different proportionality coefficients, and JR is expressed as the number of residents in each building;
carrying out classification combination on the bearing value, the number of resident people, the marked floor mark set, the living class preset value and the real-time average temperature to obtain data analysis information;
analyzing the electrical information to obtain electrical analysis information, wherein the specific steps comprise:
acquiring equipment data, power data and electric charge data in the electrical information;
setting different equipment types to correspond to different equipment preset values, matching the equipment types in the equipment data with all the equipment types to obtain corresponding equipment preset values, and marking the equipment preset values as SYi, wherein i is 1,2.. n; acquiring standard power corresponding to different device types in device data and marking the standard power as SBGi, i is 1,2.. n;
counting the standard total power of each household device and marking as BZGi, i is 1,2.. n; acquiring the actual total power of each household device in the power data and marking as SZGi, i is 1,2.. n;
acquiring an electric charge value of each household every day in the electric charge data and a total power value and a temperature mean value corresponding to the electric charge value, setting different temperature values to correspond to different preset temperature values, matching the indoor temperature mean value of each household every day with all the temperature values to acquire the corresponding preset temperature value, and marking the preset temperature value as WYi, i is 1,2. Marking the electricity charge value as DFi, i ═ 1,2.. n; marking a total power value corresponding to the electric charge value as DGZi, i-1, 2.. n;
normalizing the marked equipment preset value, standard power, standard total power, actual total power, temperature preset value, electric charge value and total power value and taking values;
the real operation value of the electrical information is obtained by using a formula, wherein the formula is as follows:
wherein Q issyExpressed as actual running values, b1, b2 and b3 are expressed as different proportionality coefficients;
classifying and combining the actual operation value and the marked equipment preset value, standard power, standard total power, actual total power, temperature preset value, electric charge value and total power value to obtain electrical analysis information;
the data analysis information and the electrical analysis information are combined to obtain a data analysis set, and the data analysis set is sent to the data processing module;
the data processing module is used for receiving and processing the data analysis set to obtain a data processing set, and the specific steps comprise:
the method comprises the following steps: receiving data analysis information and electrical analysis information in the data analysis set;
step two: acquiring a bearing value in the data analysis information and an actual operation value in the electrical analysis information;
step three: calculating and acquiring a real span coefficient of a bearing value and a real operation value by using a formula, wherein the formula is as follows:
wherein Q isskExpressed as the real span coefficient, QczExpressed as a load-bearing value, QsyThe actual operation value is represented, eta is represented as a preset actual span correction factor, and c1 and c2 are represented as different proportionality coefficients;
step four: matching the real span coefficient with a preset standard real span threshold value, and if the real span coefficient is not greater than the standard real span threshold value, judging that the building electrical state corresponding to the real span coefficient normally operates and generating a first matching signal; if the real span coefficient is larger than the standard real span threshold value, judging that the building electrical operation state corresponding to the real span coefficient is abnormal and generating a second matching signal;
step five: marking the building corresponding to the real span coefficient as a building to be tested according to the second matching signal, marking the electric appliance in the building to be tested as an electric appliance to be tested, and marking the real-time temperature mean value in the building to be tested as a temperature mean value to be tested;
step six: classifying and combining the first matching signal, the second matching signal, the marked building to be tested, the electric appliance to be tested and the temperature mean value to be tested to obtain a data processing set;
the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set, and the specific steps comprise:
receiving and analyzing a data processing set;
if the data processing set contains a second matching signal, acquiring a building to be tested, an electric appliance to be tested and a temperature average value to be tested corresponding to the second matching signal;
acquiring real-time power of an electric appliance to be tested and standard power corresponding to the electric appliance to be tested, respectively marking the real-time power and the standard power as D1 and D2, acquiring body surface temperature of the electric appliance to be tested and marking the body surface temperature as D3, and marking the mean value of the temperature to be tested as D4;
calculating the positive and negative values of each electrical appliance to be measured by using a formula, wherein the formula is as follows:
wherein Q iszyExpressing as positive and different values, expressing as preset positive and different correction factors, and expressing as different proportionality coefficients g1, g2 and g 3;
acquiring a standard normal and abnormal range preset by an electric appliance to be tested, matching the normal and abnormal value with the standard normal and abnormal range, and if the normal and abnormal value is smaller than the minimum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a first early warning signal; if the normal and abnormal values belong to the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is normal and generating a forecast signal; if the normal and abnormal values are larger than the maximum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a second early warning signal;
according to first early warning signal and second early warning signal to waiting to await measuring the building family and carry out the early warning, specific step includes:
acquiring a network address of a building to be detected corresponding to the first early warning signal and the second early warning signal, marking the network address as a communication address, acquiring connection equipment of the communication address, and sending the first early warning signal and the second early warning signal to the connection equipment by using the communication address for prompting and generating a prompting signal;
if the to-be-detected electrical appliance corresponding to the first early warning signal and the second early warning signal stops running within a preset time period after the prompt signal is generated, judging that the user receives the prompt signal and processing the prompt signal;
if the running states of the electric appliances to be tested corresponding to the first early warning signal and the second early warning signal are unchanged within a preset time period after the prompt signal is generated, judging that the prompt signal received by the user is abnormal and not processed, and performing power-off processing on the building to be tested according to the prompt signal;
the above formulas are obtained by collecting a large amount of data and performing software simulation, and the coefficients in the formulas are set by those skilled in the art according to actual conditions.
The working principle of the invention is as follows: in the embodiment of the invention, the data acquisition module, the data positioning module, the data analysis module, the data processing module and the early warning control module are used in a matched manner, so that the purposes of monitoring the electrical running state of each building in a building and early warning and regulating the building in an abnormal state can be achieved;
the data acquisition module comprises a building acquisition unit and an electrical acquisition unit, the building acquisition unit is used for acquiring data information of a building, the data information comprises floor data, building data and accommodation data, the electrical acquisition unit is used for acquiring electrical information of the building, the electrical information comprises equipment data, power data and electricity charge data, and the data information and the electrical information are sent to the data analysis module; by collecting data information of the building and electrical information of the building, effective data support can be provided for monitoring, early warning, regulation and control of electrical operating states of the building;
the data positioning module is used for positioning and processing the coordinates of the floors and the building households on the building to obtain coordinate processing information, and the coordinate processing information is sent to the data analysis module; by positioning and processing the coordinates of the floors and the building families on the building, the positions of the building families can be quickly obtained, and quick positioning, early warning and regulation and control can be carried out in an abnormal state;
receiving data information and electrical information by using a data analysis module, analyzing the data information by using a formula
Calculating and acquiring a bearing value of the building; classifying and combining the bearing value, the number of resident people, the marked floor mark set, the residence preset value and the real-time average temperature to obtain data analysis information, analyzing the electrical information, and utilizing a formula
Acquiring a real operation value of the electrical information; classifying and combining the actual operation value with the marked equipment preset value, standard power, standard total power, actual total power, temperature preset value, electricity charge value and total power value to obtain electrical analysis information, combining the data analysis information with the electrical analysis information to obtain a data analysis set, and sending the data analysis set to a data processing module; by pairsThe data information and the electrical information are calculated to obtain a bearing value and an actual operation value, so that the data information and each data item in the electrical information are linked, and the accuracy and the relevance of the data can be improved;
the data processing module is used for receiving and processing the data analysis set to obtain a data processing set, and the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set; obtaining real span coefficient by processing and calculating data analysis set, and using formula
Calculating a real span coefficient of the obtained bearing value and the real running value; analyzing the real cross coefficient to obtain the operation state of the building family, and early warning and regulating in time, and using a formula
Calculating the positive and negative values of each electrical appliance to be tested; acquiring a standard normal and abnormal range preset by an electric appliance to be tested, matching the normal and abnormal value with the standard normal and abnormal range, and if the normal and abnormal value is smaller than the minimum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a first early warning signal; if the normal and abnormal values belong to the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is normal and generating a forecast signal; if the normal and abnormal values are larger than the maximum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a second early warning signal; according to the first early warning signal and the second early warning signal, early warning is carried out on the building to be detected, fire hazard is prevented from occurring, potential safety hazards are eliminated, and the intelligence of electrical control is improved.
In the embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of the embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one control module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is to be understood that the word "comprising" does not exclude other modules or steps, and the singular does not exclude the plural. A plurality of modules or means recited in the system claims may also be implemented by one module or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.
Claims (2)
1. An intelligent building electrical control system is characterized by comprising a data acquisition module, a data positioning module, a data analysis module, a data processing module and an early warning control module;
the data acquisition module comprises a building acquisition unit and an electrical acquisition unit, the building acquisition unit is used for acquiring data information of a building, the data information comprises floor data, building data and accommodation data, the electrical acquisition unit is used for acquiring electrical information of the building, the electrical information comprises equipment data, power data and electricity charge data, and the data information and the electrical information are sent to the data analysis module;
the data positioning module is used for positioning and processing the coordinates of floors and households on the building to obtain coordinate processing information and sending the coordinate processing information to the data analysis module;
the data analysis module is used for receiving the data information and the electrical information, analyzing the data information to obtain data analysis information, analyzing the electrical information to obtain electrical analysis information, combining the data analysis information and the electrical analysis information to obtain a data analysis set, and sending the data analysis set to the data processing module;
the data processing module is used for receiving and processing the data analysis set to obtain a data processing set, and the specific steps comprise:
the method comprises the following steps: receiving data analysis information and electrical analysis information in the data analysis set;
step two: acquiring a bearing value in the data analysis information and an actual operation value in the electrical analysis information;
step three: calculating and acquiring a real span coefficient of a bearing value and a real operation value by using a formula, wherein the formula is as follows:
wherein Q isskExpressed as the real span coefficient, QczExpressed as a load-bearing value, QsyThe actual operation value is represented, eta is represented as a preset actual span correction factor, and c1 and c2 are represented as different proportionality coefficients;
step four: matching the real span coefficient with a preset standard real span threshold value, and if the real span coefficient is not greater than the standard real span threshold value, judging that the building electrical state corresponding to the real span coefficient normally operates and generating a first matching signal; if the real span coefficient is larger than the standard real span threshold value, judging that the building electrical operation state corresponding to the real span coefficient is abnormal and generating a second matching signal;
step five: marking the building corresponding to the real span coefficient as a building to be tested according to the second matching signal, marking the electric appliance in the building to be tested as an electric appliance to be tested, and marking the real-time temperature mean value in the building to be tested as a temperature mean value to be tested;
step six: classifying and combining the first matching signal, the second matching signal, the marked building to be tested, the electric appliance to be tested and the temperature mean value to be tested to obtain a data processing set;
the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set;
analyzing the data information to obtain data analysis information, wherein the specific steps comprise:
s21: acquiring floor data, building data and accommodation data in the data information;
s22: marking a plurality of floors in floor data from bottom to top to obtain a floor mark set LBi, wherein i is 1,2.. n; acquiring building users of a plurality of floors according to building data, and marking the building users from left to right to obtain a building mark set FBi, wherein i is 1,2.. n;
s23: acquiring the number of resident people and resident groups in each building in the lodging data according to the building data, setting the resident groups in different age groups to correspond to different class preset values, matching the resident groups in each building with all the resident groups according to the age groups to acquire the corresponding class preset values, and marking the corresponding class preset values as JYi, wherein i is 1,2.
S24: acquiring real-time average indoor temperature in the lodging data and marking as SWi, i is 1,2.. n;
s25: normalizing the marked household preset value and the real-time average temperature, taking values, and calculating by using a formula to obtain the bearing value of the building, wherein the formula is as follows:
wherein Q isczExpressed as a load value, mu is expressed as a preset load correction factor, a1, a2 and a3 are expressed as different proportionality coefficients, and JR is expressed as the number of residents in each building;
s26: carrying out classification combination on the bearing value, the number of resident people, the marked floor mark set, the living class preset value and the real-time average temperature to obtain data analysis information;
analyzing the electrical information to obtain electrical analysis information, wherein the specific steps comprise:
s31: acquiring equipment data, power data and electric charge data in the electrical information;
s32: setting different equipment types to correspond to different equipment preset values, matching the equipment types in the equipment data with all the equipment types to obtain corresponding equipment preset values, and marking the equipment preset values as SYi, wherein i is 1,2.. n; acquiring standard power corresponding to different device types in device data and marking the standard power as SBGi, i is 1,2.. n;
s33: counting the standard total power of each household device and marking as BZGi, i is 1,2.. n; acquiring the actual total power of each household device in the power data and marking as SZGi, i is 1,2.. n;
s34: acquiring an electric charge value of each household every day in the electric charge data and a total power value and a temperature mean value corresponding to the electric charge value, setting different temperature values to correspond to different preset temperature values, matching the indoor temperature mean value of each household every day with all the temperature values to acquire the corresponding preset temperature value, and marking the preset temperature value as WYi, i is 1,2. Marking the electricity charge value as DFi, i ═ 1,2.. n; marking a total power value corresponding to the electric charge value as DGZi, i-1, 2.. n;
s35: normalizing the marked equipment preset value, standard power, standard total power, actual total power, temperature preset value, electric charge value and total power value and taking values;
s36: the real operation value of the electrical information is obtained by using a formula, wherein the formula is as follows:
wherein Q issyExpressed as actual running values, b1, b2 and b3 are expressed as different proportionality coefficients;
s37: classifying and combining the actual operation value and the marked equipment preset value, standard power, standard total power, actual total power, temperature preset value, electric charge value and total power value to obtain electrical analysis information;
the early warning control module is used for early warning and regulating and controlling the electrical state of the building according to the data processing set, and the specific steps comprise:
s41: receiving and analyzing a data processing set;
s42: if the data processing set contains a second matching signal, acquiring a building to be tested, an electric appliance to be tested and a temperature average value to be tested corresponding to the second matching signal;
s43: acquiring real-time power of an electric appliance to be tested and standard power corresponding to the electric appliance to be tested, respectively marking the real-time power and the standard power as D1 and D2, acquiring body surface temperature of the electric appliance to be tested and marking the body surface temperature as D3, and marking the mean value of the temperature to be tested as D4;
s44: calculating the positive and negative values of each electrical appliance to be measured by using a formula, wherein the formula is as follows:
wherein Q iszyExpressing as positive and different values, expressing as preset positive and different correction factors, and expressing as different proportionality coefficients g1, g2 and g 3;
s45: acquiring a standard normal and abnormal range preset by an electric appliance to be tested, matching the normal and abnormal value with the standard normal and abnormal range, and if the normal and abnormal value is smaller than the minimum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a first early warning signal; if the normal and abnormal values belong to the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is normal and generating a forecast signal; if the normal and abnormal values are larger than the maximum value of the standard normal and abnormal range, judging that the running power of the electric appliance to be tested is abnormal and generating a second early warning signal;
s46: and early warning is carried out on the building to be detected according to the first early warning signal and the second early warning signal.
2. The building intelligent electrical control system of claim 1, wherein the early warning is performed to the building to be tested according to the first early warning signal and the second early warning signal, and the specific steps comprise:
s51: acquiring a network address of a building to be detected corresponding to the first early warning signal and the second early warning signal, marking the network address as a communication address, acquiring connection equipment of the communication address, and sending the first early warning signal and the second early warning signal to the connection equipment by using the communication address for prompting and generating a prompting signal;
s52: if the to-be-detected electrical appliance corresponding to the first early warning signal and the second early warning signal stops running within a preset time period after the prompt signal is generated, judging that the user receives the prompt signal and processing the prompt signal;
s53: and if the running states of the electric appliances to be tested corresponding to the first early warning signal and the second early warning signal are unchanged within a preset time period after the prompt signal is generated, judging that the prompt signal received by the user is abnormal and not processed, and performing power-off processing on the building to be tested according to the prompt signal.
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| CN116697647B (en) * | 2023-06-13 | 2023-12-15 | 苏州智允机电系统集成科技有限公司 | Energy-saving management analysis system of building heat pump |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101434210B1 (en) * | 2014-04-14 | 2014-08-26 | 주식회사 내일솔루션 | Carbon emissions building automatic control system |
| CN105068438A (en) * | 2015-08-06 | 2015-11-18 | 国网山东省电力公司 | Large-scale office building intelligent electricity utilization electrical appliance control system and method |
| CN210323870U (en) * | 2019-05-21 | 2020-04-14 | 新世界发展有限公司 | Building energy consumption management system |
| JP2020061875A (en) * | 2018-10-10 | 2020-04-16 | 中部電力株式会社 | Power amount prediction system of air conditioner and operation system of air conditioner |
| CN111337026A (en) * | 2020-02-17 | 2020-06-26 | 安徽建筑大学 | Indoor positioning system |
| CN111639462A (en) * | 2020-05-29 | 2020-09-08 | 桂林电子科技大学 | Building indoor thermal comfort prediction method in natural ventilation environment based on deep belief neural network |
| CN111830891A (en) * | 2020-07-28 | 2020-10-27 | 杭州武盛广告制作有限公司 | Community security monitoring system based on data acquisition |
| CN112140891A (en) * | 2020-09-29 | 2020-12-29 | 安徽花山街网络科技有限公司 | Ternary lithium battery work monitoring system for new energy automobile based on Internet of things |
| CN112165160A (en) * | 2020-08-10 | 2021-01-01 | 上海有间建筑科技有限公司 | Comprehensive monitoring system for power consumption of intelligent building |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109407525A (en) * | 2018-07-26 | 2019-03-01 | 昆山智汇无界信息技术有限公司 | Building energy saving automatic control system and method based on artificial intelligence |
| CN110097476A (en) * | 2019-06-11 | 2019-08-06 | 国网江苏省电力有限公司苏州供电分公司 | Building energy management apparatus and method based on energy consumption fining statistics with publication |
-
2021
- 2021-01-29 CN CN202110125979.5A patent/CN112947158B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101434210B1 (en) * | 2014-04-14 | 2014-08-26 | 주식회사 내일솔루션 | Carbon emissions building automatic control system |
| CN105068438A (en) * | 2015-08-06 | 2015-11-18 | 国网山东省电力公司 | Large-scale office building intelligent electricity utilization electrical appliance control system and method |
| JP2020061875A (en) * | 2018-10-10 | 2020-04-16 | 中部電力株式会社 | Power amount prediction system of air conditioner and operation system of air conditioner |
| CN210323870U (en) * | 2019-05-21 | 2020-04-14 | 新世界发展有限公司 | Building energy consumption management system |
| CN111337026A (en) * | 2020-02-17 | 2020-06-26 | 安徽建筑大学 | Indoor positioning system |
| CN111639462A (en) * | 2020-05-29 | 2020-09-08 | 桂林电子科技大学 | Building indoor thermal comfort prediction method in natural ventilation environment based on deep belief neural network |
| CN111830891A (en) * | 2020-07-28 | 2020-10-27 | 杭州武盛广告制作有限公司 | Community security monitoring system based on data acquisition |
| CN112165160A (en) * | 2020-08-10 | 2021-01-01 | 上海有间建筑科技有限公司 | Comprehensive monitoring system for power consumption of intelligent building |
| CN112140891A (en) * | 2020-09-29 | 2020-12-29 | 安徽花山街网络科技有限公司 | Ternary lithium battery work monitoring system for new energy automobile based on Internet of things |
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