CN111198545B - Intelligent building indoor air environment maintenance system and method - Google Patents
Intelligent building indoor air environment maintenance system and method Download PDFInfo
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Abstract
The invention discloses an indoor air environment maintenance system and method for an intelligent building, which divides the intelligent building into a plurality of building spaces, and the system comprises: a plurality of groups for distributed layout and computation; each group comprises a regulation and control decision module, a plurality of air quality sensors and a plurality of air environment maintenance devices, wherein the air quality sensors are used for collecting air quality data of corresponding building spaces, the air environment maintenance devices are used for maintaining and controlling the air quality of the corresponding building spaces, the regulation and control decision module is used for analyzing, calculating and determining a regulation and control strategy according to the air quality data of the corresponding building spaces and issuing regulation and control instructions to the air environment maintenance devices in the corresponding building spaces; and the central server is used for processing preset tasks, transmitting data among groups, monitoring the running state of each group and adjusting the group structure. The method has the advantages of reducing communication load, improving communication real-time performance, fully optimizing each maintenance device in a targeted manner and the like.
Description
Technical Field
The invention relates to the field of air quality maintenance and big data analysis, in particular to an intelligent building indoor air environment maintenance system and method.
Background
In recent years, the air quality is deteriorated, the content of pollutants such as dust, PM2.5 and the like in the air is gradually increased, the number of days of serious pollution is increased all the year round, various respiratory diseases and even cancers appear, and the health of people is seriously influenced. Especially in indoor environments with relatively dense personnel and poor air flow, not only are the above-mentioned pollutants present, but also residues that are volatile during decoration, viruses and various bacteria residues carried by patients, etc. may be present. Aiming at the condition of poor indoor air quality, air purification equipment can be installed indoors, and technical means such as a filter screen and a photocatalyst are applied to realize air filtration, sterilization and harmlessness.
The intelligent building is based on the Internet of things and is characterized in that various sensors are widely distributed in the building space range, the collection and transmission of sensing data are realized through the Internet of things, and the adjustment and control of internal facilities of the building are performed on the basis of the intelligent analysis of the sensing data. For example, in an air maintenance device in an intelligent building, a large number of air quality sensors need to be arranged in a building space, the collection and transmission of air quality sensing data are realized through the internet of things, and the air maintenance device is regulated and controlled after the air quality sensing data are analyzed.
At present, the intelligent building air maintenance based on the internet of things has the following defects: 1. because the air has fluidity and the limitation of a detection means, the stability and the accuracy of the air quality sensing data are always poor, the acquired sensing data has large change amplitude, high change frequency and more invalid data, and can bring much inconvenience to the regulation and control of air environment equipment; meanwhile, due to the mobility of air, the timeliness of data is short, and the frequency of data acquisition and detection needs to be increased, so that the problems of large quantity of sensing data, heavy communication burden and the like exist. 2. The effective action range of each air maintenance device is limited, generally about ten meters to thirty meters, so that a large number of air maintenance devices need to be installed in a building, the spatial layout of each building is different, the quantity difference is larger, if all collected sensing data are uniformly uploaded to a background server for analysis, a regulation and control strategy is determined and then is issued to each front-end air maintenance device, the real-time performance is poor, the regulation and control delay time is long, and the regulation and control strategy is difficult to be fully optimized for each maintenance device.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to: the intelligent building indoor air environment maintenance system and method are designed to solve the problems that in the prior art, communication burden of indoor air quality detection data is heavy, full optimization of each maintenance device is difficult, and the like, and have the advantages that communication overhead and communication burden of indoor air quality acquisition data can be reduced, communication real-time performance is good, full optimization of each maintenance device can be achieved, and the like.
An intelligent building indoor air environment maintenance system divides an intelligent building into a plurality of building spaces, comprising:
the groups correspond to the building spaces one by one and are used for distributed layout and calculation;
each group comprises a regulation and control decision module, a plurality of air quality sensors and a plurality of air environment maintenance devices, wherein the air quality sensors are used for collecting air quality data of corresponding building spaces, the air environment maintenance devices are used for maintaining and controlling the air quality of the corresponding building spaces, the regulation and control decision module is used for analyzing, calculating and determining a regulation and control strategy according to the air quality data of the corresponding building spaces, and issuing regulation and control instructions to the air environment maintenance devices in the corresponding building spaces;
and the central server is used for processing preset tasks, transmitting data among groups, monitoring the running state of each group and adjusting the group structure.
Further, the regulation and control decision module comprises a BP neural network, the input quantity of the BP neural network is air quality data collected by an air quality sensor in the building space corresponding to the regulation and control decision module, and the output quantity of the BP neural network is a regulation and control preset value for each air environment maintenance device in the group corresponding to the building space.
Further, each group comprises at least two regulation decision modules, a master regulation decision module and at least one slave regulation decision module; the main regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data and issuing a regulation instruction to the air environment maintenance equipment corresponding to the building space according to the regulation strategy; and the slave regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data.
Further, the master regulation decision module and the slave regulation decision module are in communication connection and used for data sharing, and when a preset condition is met, master-slave switching is performed between the master regulation decision module and the slave regulation decision module.
Further, the number of the regulation and control decision modules configured in the corresponding building space is determined according to any one or more of the number of air quality sensors in the corresponding building space, the number of air environment maintenance devices, the size of the corresponding building space, and the complexity of the corresponding building space.
An intelligent building indoor air environment maintenance method comprises the following steps:
dividing an intelligent building into a plurality of building spaces;
carrying out distributed layout and calculation by adopting a plurality of groups, wherein the groups correspond to the building spaces one by one;
each group comprises a regulation and control decision module, a plurality of air quality sensors and a plurality of air environment maintenance devices, wherein the air quality sensors acquire air quality data corresponding to building spaces, the air environment maintenance devices maintain and control the air quality of the corresponding building spaces, the regulation and control decision module analyzes, calculates and determines a regulation and control strategy according to the air quality data of the corresponding building spaces and issues regulation and control instructions to the air environment maintenance devices in the corresponding building spaces;
and processing preset tasks, transmitting data among groups, monitoring the running state of each group and adjusting the group structure through the central server.
Further, the regulation and control decision module comprises a BP neural network, the input quantity of the BP neural network is air quality data collected by an air quality sensor in the building space corresponding to the regulation and control decision module, and the output quantity of the BP neural network is a regulation and control preset value for each air environment maintenance device in the group corresponding to the building space.
Further, each group comprises at least two regulation decision modules, a master regulation decision module and at least one slave regulation decision module; the main regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data and issuing a regulation instruction to the air environment maintenance equipment corresponding to the building space according to the regulation strategy; and the slave regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data.
Further, the master regulation decision module and the slave regulation decision module are in communication connection and used for data sharing, and when a preset condition is met, master-slave switching is performed between the master regulation decision module and the slave regulation decision module.
Further, the number of the regulation and control decision modules configured in the corresponding building space is determined according to any one or more of the number of air quality sensors in the corresponding building space, the number of air environment maintenance devices, the size of the corresponding building space, and the complexity of the corresponding building space.
Compared with the prior art, the invention has the following advantages:
the invention provides an indoor air environment maintenance system and method for an intelligent building, which are characterized in that an air quality sensor and air environment maintenance equipment in the intelligent building are distributed and calculated, a regulation and control decision module in each group only analyzes and calculates sensing data collected by the front end in the group, a corresponding regulation and control strategy is formulated, and the air environment maintenance system and method are fully optimized only for each air maintenance equipment in the group. The method has the advantages of reducing communication load, improving communication real-time performance, fully optimizing each maintenance device in a targeted manner and the like.
Drawings
FIG. 1 is a system block diagram of an indoor air environment maintenance system of an intelligent building according to an embodiment of the present invention;
fig. 2 is a flowchart of an indoor air environment maintenance method for an intelligent building according to a second embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
referring to fig. 1, an intelligent building indoor air environment maintenance system includes:
the intelligent building is divided into a plurality of building spaces, specifically, the intelligent building is divided into one building space according to floors, rooms, stairs and the like, for example, one floor is one building space, one room is one building space, and each stair is a building space separately.
The groups correspond to the building spaces one by one and are used for distributed layout and calculation; specifically, a certain number of air quality sensors and air environment maintenance devices at the front end are configured into a group, and one group corresponds to one building space, for example, all the air quality sensors and air environment maintenance devices on the same floor are configured into one group, or the air quality sensors and air environment maintenance devices in the same room are configured into one group, or the air quality sensors and air environment maintenance devices in the installation positions close to each other are configured into one group. And the distributed structure that one group corresponds to one building space is adopted for layout and calculation.
Every group is including regulation and control decision-making module, a plurality of air quality sensor and a plurality of air circumstance maintenance facilities, air quality sensor is used for gathering the air quality data that corresponds the building space, air circumstance maintenance facilities is used for maintaining the air quality that the regulation and control corresponds the building space, regulation and control decision-making module is used for according to the air quality data analysis of corresponding building space, calculation and confirm the regulation and control strategy to give the regulation and control instruction to the air circumstance maintenance facilities that correspond in the building space. Specifically, the regulation and control decision module in each group performs calculation, analysis and decision according to data provided by the air quality sensors in the group, so as to determine the air quality regulation and control strategy of the group, and then sends a regulation and control instruction to the air maintenance equipment of the group.
And the central server is used for processing preset tasks, transmitting data among groups, monitoring the running state of each group and adjusting the group structure. Specifically, the preset task mainly refers to a task that a group cannot process or has no authority to process; when the group needs to interact data and instructions, the group can be transferred through the central server; the running states of all groups can be monitored in real time through the central server; when the number of sensor/air maintenance devices in the group needs to be increased or decreased, the groups need to be merged, the groups need to be cancelled and the like, the operation can be completed through the central server.
According to the indoor air environment maintenance system of the intelligent building, the air quality sensors and the air environment maintenance equipment in the intelligent building are distributed and calculated, the regulation and control decision modules in each group analyze and calculate the sensing data collected by the front end in the group, then the regulation and control strategy is made for the air environment maintenance equipment in the group, and the air environment maintenance system is fully optimized for each air maintenance equipment in the group. Therefore, most of the sensing data collected by the front end are transmitted, analyzed and processed in the group, only a small part of the sensing data needs to be communicated with the central server for processing, the communication overhead and the communication burden are greatly reduced, the communication load is reduced, the communication real-time performance is improved, and each maintenance device can be optimized in a targeted manner.
In the above intelligent building indoor air environment maintenance system, the regulation and control decision module includes a BP neural network, the input of the BP neural network is air quality data collected by an air quality sensor in a building space corresponding to the regulation and control decision module, and the output is a regulation and control preset value for each air environment maintenance device in a group corresponding to the building space. Specifically, the regulation and control decision module generates decision results according to a BP neural network training model, the internal structure of each regulation and control decision module is a BP neural network, a sensing data set acquired by all air quality sensors of the group and obtained by the regulation and control decision module is used as input quantity, the decision results comprise regulation and control instructions for each air environment maintenance device in the group, and the regulation and control preset value for each air environment maintenance device of the group is the output quantity of the BP neural network. In specific implementation, the BP neural network training model needs to be simulated and trained by a certain number of samples, and after multiple times of training, a decision result can be output according to sensing data provided by the air quality sensors in the group.
Each group of the intelligent building indoor air environment maintenance system at least comprises two regulation and control decision modules, a master regulation and control decision module and at least one slave regulation and control decision module; the main regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data and issuing a regulation instruction to the air environment maintenance equipment corresponding to the building space according to the regulation strategy; and the slave regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data. Specifically, due to the cost, the software and hardware of the regulation and control decision module in each group cannot be configured according to the standard of the central server, so that the data acquisition capacity and the calculation and analysis capacity of the regulation and control decision module are far inferior to those of the central server, and since the air itself has the mobility and the restriction of the detection means, the stability of the air quality sensing data is poor, and an erroneous regulation and control decision may occur. Therefore, two or more than two regulation decision modules, one master regulation decision module and one or more slave regulation decision modules are arranged in each group, the master regulation decision module and the slave regulation decision modules can analyze, calculate and determine a regulation strategy according to air quality data, but only the master regulation decision module issues a regulation instruction to the air environment maintenance equipment of the group, and the slave regulation decision modules do not issue the regulation instruction.
In the system for maintaining the indoor air environment of the intelligent building, the master regulation decision module and the slave regulation decision module are in communication connection and used for data sharing, and when a preset condition is met, master-slave switching is performed between the master regulation decision module and the slave regulation decision module. Particularly, the main regulation decision module and the slave regulation decision module share decision results mutually, so that the decision precision can be improved, and the decision error rate can be effectively prevented. The master-slave status switching can be carried out between the master regulation decision module and the slave regulation decision module through a voting score mechanism, and specifically, a voting module is arranged in each regulation decision module in the group and stores the same voting rule; according to the decision results exchanged by the master regulation decision module and the slave regulation decision module, a voting score result is given according to a voting rule; when the regulation and control decision module corresponding to the decision result with the highest voting score is not the current main decision module, the backup decision module with the highest voting score is automatically upgraded to the main regulation and control decision module, and a regulation and control instruction is issued by the main regulation and control decision module; and the original main regulation decision module is changed into a backup decision module.
In specific implementation, the scoring mechanism of the voting rule includes the following mechanisms: A. the multiple regulation and control decision modules acquire sensing data of the air quality sensors of the group according to a preset time interval, for example, if the first regulation and control decision module T0 acquires the sensing data of the air quality sensors at the moment, the second regulation and control decision module T1 acquires the sensing data again at the moment, and a preset time interval is formed between the first regulation and control decision module T1 and the second regulation and control decision module; B. the regulation and control decision module makes a decision result according to the acquired sensing data, and analyzes the executability of the air quality maintenance equipment in each group according to the decision result of each regulation and control decision module, for example, if the decision result exceeds the allowable execution range of the air quality maintenance equipment, or the air quality maintenance equipment cannot reach the target required by the decision result within the effective time, for example, if the air quality maintenance equipment cannot reach the air maintenance target predetermined by the decision result within the effective time, the executability is considered to be 0; correspondingly, if the effective time required for reaching the decision result requirement target is longer, the lower the executable degree is, and the lower the executable degree is, the lower the score of the regulation and control decision module is; C. under the condition that the group comprises more than three regulation and control decision modules, the decision similarity indexes of all the regulation and control decision modules can be calculated, and if the similarity indexes of one decision result and other decision results are low, the score is low; D. and obtaining the voting score result of each decision result according to the score obtained in the step B or the accumulation of the scores obtained in the step B and the step C. T0 and T1 represent time.
According to the intelligent building indoor air environment maintenance system, the number of the regulation and control decision modules configured in the corresponding building space is determined according to any one or more of the number of the air quality sensors, the number of the air environment maintenance equipment, the size of the corresponding building space and the complexity of the corresponding building space. Specifically, the number M of air quality sensors in one building space, the number N of air environment maintenance devices, the size O of the corresponding building space, and the complexity P of the corresponding building space are taken as four variable factors. For example, when M is less than M1, N is less than N1, O is less than O1, and P is less than P1, two regulatory decision modules, one master regulatory decision module and one slave regulatory decision module, are configured within the group; when M is greater than M1 and less than M2, N is greater than N1 and less than N2, O is greater than O1 and less than O2, and P is greater than P1 and less than P2, configuring three regulatory decision modules, one master regulatory decision module and two slave regulatory decision modules in the group; when M is greater than M2 and less than M3, N is greater than N2 and less than N3, O is greater than O2 and less than O3, and P is greater than P2 and less than P3, configuring four regulatory decision modules, one master regulatory decision module and three slave regulatory decision modules in the group; the other three factors are used in the same way. Further, in practical implementation, the crossing of the sizes of the factors is the most common case, and weighting coefficients may be set for the factors, for example, assuming that the weighting coefficients of M and N are 1 and the weighting coefficients of O and P are 2, and when M is less than M1, N is greater than N1 and less than N2, O is greater than O1 and less than O3, and P is greater than P1 and less than P2, the weighting is 1+ 2+ 3+ 2+ 13; presetting a weight value, wherein the weight value is 0-10, and configuring two regulation and control decision modules, a master regulation and control decision module and a slave regulation and control decision module in a group; if the weight value is 11-20, configuring three regulation and control decision modules, a master regulation and control decision module and two slave regulation and control decision modules in the group; and if the weight value is 21-30, four regulation and control decision modules, a master regulation and control decision module and three slave regulation and control decision modules are configured in the group. Still further, in practice, there may be other factors, which may be used in combination according to the above-described method. The complexity mainly refers to obstacles such as furniture, plants supported and planted by the building and the like in the building space, and the complexity of the building space can be evaluated through the number of the obstacles in specific implementation.
Referring to fig. 2, an intelligent building indoor air environment maintenance method includes the following steps:
dividing an intelligent building into a plurality of building spaces; specifically, the intelligent building is divided into building spaces of one floor according to floors, rooms, stairs and the like, for example, one floor is a building space, one room is a building space, and each stair is a building space separately.
Carrying out distributed layout and calculation by adopting a plurality of groups, wherein the groups correspond to the building spaces one by one; specifically, a certain number of air quality sensors and air environment maintenance devices at the front end are configured into a group, and one group corresponds to one building space, for example, all the air quality sensors and air environment maintenance devices on the same floor are configured into one group, or the air quality sensors and air environment maintenance devices in the same room are configured into one group, or the air quality sensors and air environment maintenance devices in the installation positions close to each other are configured into one group. And the distributed structure that one group corresponds to one building space is adopted for layout and calculation.
Each group comprises a regulation and control decision module, a plurality of air quality sensors and a plurality of air environment maintenance devices, wherein the air quality sensors acquire air quality data corresponding to building spaces, the air environment maintenance devices maintain and control the air quality of the corresponding building spaces, the regulation and control decision module analyzes, calculates and determines a regulation and control strategy according to the air quality data of the corresponding building spaces and issues regulation and control instructions to the air environment maintenance devices in the corresponding building spaces; specifically, the regulation and control decision module in each group performs calculation, analysis and decision according to data provided by the air quality sensors in the group, so as to determine the air quality regulation and control strategy of the group, and then sends a regulation and control instruction to the air maintenance equipment of the group.
And processing preset tasks, transmitting data among groups, monitoring the running state of each group and adjusting the group structure through the central server. Specifically, the preset task mainly refers to a task that a group cannot process or has no authority to process; when the group needs to interact data and instructions, the group can be transferred through the central server; the running states of all groups can be monitored in real time through the central server; when the number of sensor/air maintenance devices in the group needs to be increased or decreased, the groups need to be merged, the groups need to be cancelled and the like, the operation can be completed through the central server.
According to the indoor air environment maintenance method for the intelligent building, the air quality sensors and the air environment maintenance equipment in the intelligent building are distributed and calculated, the regulation and control decision module in each group analyzes and calculates the sensing data collected by the front end in the group, then the regulation and control strategy is formulated for the air environment maintenance equipment in the group, and the air environment maintenance equipment in the group is fully optimized. Therefore, most of the sensing data collected by the front end are transmitted, analyzed and processed in the group, only a small part of the sensing data needs to be communicated with the central server for processing, the communication overhead and the communication burden are greatly reduced, the communication load is reduced, the communication real-time performance is improved, and each maintenance device can be optimized in a targeted manner.
According to the method for maintaining the indoor air environment of the intelligent building, the regulation and control decision module comprises a BP neural network, the input quantity of the BP neural network is the air quality data collected by the air quality sensor in the building space corresponding to the regulation and control decision module, and the output quantity of the BP neural network is the regulation and control preset value of each air environment maintenance device in the group corresponding to the building space. Specifically, the regulation and control decision module generates decision results according to a BP neural network training model, the internal structure of each regulation and control decision module is a BP neural network, a sensing data set acquired by all air quality sensors of the group and obtained by the regulation and control decision module is used as input quantity, the decision results comprise regulation and control instructions for each air environment maintenance device in the group, and the regulation and control preset value for each air environment maintenance device of the group is the output quantity of the BP neural network. In specific implementation, the BP neural network training model needs to be simulated and trained by a certain number of samples, and after multiple times of training, a decision result can be output according to sensing data provided by the air quality sensors in the group.
According to the method for maintaining the indoor air environment of the intelligent building, each group at least comprises two regulation and control decision modules, a master regulation and control decision module and at least one slave regulation and control decision module; the main regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data and issuing a regulation instruction to the air environment maintenance equipment corresponding to the building space according to the regulation strategy; and the slave regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data. Specifically, due to the cost, the software and hardware of the regulation and control decision module in each group cannot be configured according to the standard of the central server, so that the data acquisition capacity and the calculation and analysis capacity of the regulation and control decision module are far inferior to those of the central server, and since the air itself has the mobility and the restriction of the detection means, the stability of the air quality sensing data is poor, and an erroneous regulation and control decision may occur. Therefore, two or more than two regulation decision modules, one master regulation decision module and one or more slave regulation decision modules are arranged in each group, the master regulation decision module and the slave regulation decision modules can analyze, calculate and determine a regulation strategy according to air quality data, but only the master regulation decision module issues a regulation instruction to the air environment maintenance equipment of the group, and the slave regulation decision modules do not issue the regulation instruction.
According to the method for maintaining the indoor air environment of the intelligent building, the master regulation decision module and the slave regulation decision module are in communication connection and used for data sharing, and master-slave switching is performed between the master regulation decision module and the slave regulation decision module when preset conditions are met. Particularly, the main regulation decision module and the slave regulation decision module share decision results mutually, so that the decision precision can be improved, and the decision error rate can be effectively prevented. The master-slave status switching can be carried out between the master regulation decision module and the slave regulation decision module through a voting score mechanism, and specifically, a voting module is arranged in each regulation decision module in the group and stores the same voting rule; according to the decision results exchanged by the master regulation decision module and the slave regulation decision module, a voting score result is given according to a voting rule; when the regulation and control decision module corresponding to the decision result with the highest voting score is not the current main decision module, the backup decision module with the highest voting score is automatically upgraded to the main regulation and control decision module, and a regulation and control instruction is issued by the main regulation and control decision module; and the original main regulation decision module is changed into a backup decision module.
In specific implementation, the scoring mechanism of the voting rule includes the following mechanisms: A. the multiple regulation and control decision modules acquire sensing data of the air quality sensors of the group according to a preset time interval, for example, if the first regulation and control decision module T0 acquires the sensing data of the air quality sensors at the moment, the second regulation and control decision module T1 acquires the sensing data again at the moment, and a preset time interval is formed between the first regulation and control decision module T1 and the second regulation and control decision module; B. the regulation and control decision module makes a decision result according to the acquired sensing data, and analyzes the executability of the air quality maintenance equipment in each group according to the decision result of each regulation and control decision module, for example, if the decision result exceeds the allowable execution range of the air quality maintenance equipment, or the air quality maintenance equipment cannot reach the target required by the decision result within the effective time, for example, if the air quality maintenance equipment cannot reach the air maintenance target predetermined by the decision result within the effective time, the executability is considered to be 0; correspondingly, if the effective time required for reaching the decision result requirement target is longer, the lower the executable degree is, and the lower the executable degree is, the lower the score of the regulation and control decision module is; C. under the condition that the group comprises more than three regulation and control decision modules, the decision similarity indexes of all the regulation and control decision modules can be calculated, and if the similarity indexes of one decision result and other decision results are low, the score is low; D. and obtaining the voting score result of each decision result according to the score obtained in the step B or the accumulation of the scores obtained in the step B and the step C. T0 and T1 represent time.
According to the method for maintaining the indoor air environment of the intelligent building, the number of the regulation and control decision modules configured in the corresponding building space is determined according to any one or more of the number of the air quality sensors, the number of the air environment maintenance equipment, the size of the corresponding building space and the complexity of the corresponding building space. Specifically, the number M of air quality sensors in one building space, the number N of air environment maintenance devices, the size O of the corresponding building space, and the complexity P of the corresponding building space are taken as four variable factors. For example, when M is less than M1, N is less than N1, O is less than O1, and P is less than P1, two regulatory decision modules, one master regulatory decision module and one slave regulatory decision module, are configured within the group; when M is greater than M1 and less than M2, N is greater than N1 and less than N2, O is greater than O1 and less than O2, and P is greater than P1 and less than P2, configuring three regulatory decision modules, one master regulatory decision module and two slave regulatory decision modules in the group; when M is greater than M2 and less than M3, N is greater than N2 and less than N3, O is greater than O2 and less than O3, and P is greater than P2 and less than P3, configuring four regulatory decision modules, one master regulatory decision module and three slave regulatory decision modules in the group; the other three factors are used in the same way. Further, in practical implementation, the crossing of the sizes of the factors is the most common case, and weighting coefficients may be set for the factors, for example, assuming that the weighting coefficients of M and N are 1 and the weighting coefficients of O and P are 2, and when M is less than M1, N is greater than N1 and less than N2, O is greater than O1 and less than O3, and P is greater than P1 and less than P2, the weighting is 1+ 2+ 3+ 2+ 13; presetting a weight value, wherein the weight value is 0-10, and configuring two regulation and control decision modules, a master regulation and control decision module and a slave regulation and control decision module in a group; if the weight value is 11-20, configuring three regulation and control decision modules, a master regulation and control decision module and two slave regulation and control decision modules in the group; and if the weight value is 21-30, four regulation and control decision modules, a master regulation and control decision module and three slave regulation and control decision modules are configured in the group. Still further, in practice, there may be other factors, which may be used in combination according to the above-described method. The complexity mainly refers to obstacles such as furniture, plants supported and planted by the building and the like in the building space, and the complexity of the building space can be evaluated through the number of the obstacles in specific implementation.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the protection scope of the present invention.
Claims (6)
1. The utility model provides an intelligence building indoor air circumstance maintenance system which characterized in that divides intelligent building into a plurality of building spaces, includes:
the groups correspond to the building spaces one by one and are used for distributed layout and calculation;
each group comprises a regulation and control decision module, a plurality of air quality sensors and a plurality of air environment maintenance devices, wherein the air quality sensors are used for collecting air quality data of corresponding building spaces, the air environment maintenance devices are used for maintaining and controlling the air quality of the corresponding building spaces, the regulation and control decision module is used for analyzing, calculating and determining a regulation and control strategy according to the air quality data of the corresponding building spaces, and issuing regulation and control instructions to the air environment maintenance devices in the corresponding building spaces;
the central server is used for processing preset tasks, transmitting data among groups, monitoring the running state of each group and adjusting the group structure;
each group comprises more than three regulation decision modules, a master regulation decision module and at least two slave regulation decision modules; the main regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data and issuing a regulation instruction to the air environment maintenance equipment corresponding to the building space according to the regulation strategy; the slave regulation and control decision module is used for analyzing, calculating and determining a regulation and control strategy according to the air quality data, and the slave regulation and control decision module does not reach a regulation and control instruction;
the master regulation decision module and the slave regulation decision module are in communication connection and used for data sharing, and master-slave switching is performed between the master regulation decision module and the slave regulation decision module when preset conditions are met;
each regulation and control decision module in the group is provided with a voting module which stores the same voting rule; according to the decision results exchanged by the master regulation decision module and the slave regulation decision module, a voting score result is given according to a voting rule; when the regulation and control decision module corresponding to the decision result with the highest voting score is not the current main decision module, the backup decision module with the highest voting score is automatically upgraded to the main regulation and control decision module, and a regulation and control instruction is issued by the main regulation and control decision module; the original main regulation decision module is changed into a backup decision module;
the scoring mechanism of the voting rule comprises the following mechanisms: A. the control decision modules collect sensing data of the air quality sensors of the group according to a preset time interval; B. the control decision module makes a decision result according to the acquired sensing data, analyzes the executability of the air quality maintenance equipment in the group for the decision result of each control decision module, determines that the executability is 0 if the decision result exceeds the allowable execution range of the air quality maintenance equipment or the air quality maintenance equipment cannot meet the target required by the decision result within the effective time, and determines that the executability is lower if the effective time required for meeting the target required by the decision result is longer; the lower the executable degree is, the lower the score of the regulation and control decision module is; C. under the condition that the group comprises more than three regulation and control decision modules, the decision similarity indexes of all the regulation and control decision modules can be calculated, and if the similarity indexes of one decision result and other decision results are low, the score is low; D. and obtaining the voting score result of each decision result according to the score obtained in the step B or the accumulation of the scores obtained in the step B and the step C.
2. The intelligent indoor air environment maintenance system for the building as claimed in claim 1, wherein the control decision module comprises a BP neural network, the input of the BP neural network is the air quality data collected by the air quality sensor in the building space corresponding to the control decision module, and the output is the preset control value for each air environment maintenance device in the group corresponding to the building space.
3. The intelligent building indoor air environment maintenance system according to claim 1, wherein the number of the regulation decision modules configured in the corresponding building space is determined according to any one or more of the number of air quality sensors in the corresponding building space, the number of air environment maintenance devices, the size of the corresponding building space, and the complexity of the corresponding building space.
4. An intelligent building indoor air environment maintenance method is characterized by comprising the following steps:
dividing an intelligent building into a plurality of building spaces;
carrying out distributed layout and calculation by adopting a plurality of groups, wherein the groups correspond to the building spaces one by one; each group comprises a regulation and control decision module, a plurality of air quality sensors and a plurality of air environment maintenance devices, wherein the air quality sensors acquire air quality data corresponding to building spaces, the air environment maintenance devices maintain and control the air quality of the corresponding building spaces, the regulation and control decision module analyzes, calculates and determines a regulation and control strategy according to the air quality data of the corresponding building spaces and issues regulation and control instructions to the air environment maintenance devices in the corresponding building spaces; each group comprises more than three regulation decision modules, a master regulation decision module and at least two slave regulation decision modules; the main regulation decision module is used for analyzing, calculating and determining a regulation strategy according to the air quality data and issuing a regulation instruction to the air environment maintenance equipment corresponding to the building space according to the regulation strategy; the slave regulation and control decision module is used for analyzing, calculating and determining a regulation and control strategy according to the air quality data, and the slave regulation and control decision module does not reach a regulation and control instruction;
the master regulation decision module is in communication connection with the slave regulation decision module, data sharing is carried out, and master-slave switching is carried out between the master regulation decision module and the slave regulation decision module when preset conditions are met;
according to the decision results exchanged by the master regulation decision module and the slave regulation decision module, a voting score result is given according to a voting rule; when the regulation and control decision module corresponding to the decision result with the highest voting score is not the current main decision module, the backup decision module with the highest voting score is automatically upgraded to the main regulation and control decision module, and a regulation and control instruction is issued by the main regulation and control decision module; the original main regulation decision module is changed into a backup decision module;
the scoring mechanism of the voting rule comprises the following mechanisms: A. the control decision modules collect sensing data of the air quality sensors of the group according to a preset time interval; B. the control decision module makes a decision result according to the acquired sensing data, analyzes the executability of the air quality maintenance equipment in the group for the decision result of each control decision module, determines that the executability is 0 if the decision result exceeds the allowable execution range of the air quality maintenance equipment or the air quality maintenance equipment cannot meet the target required by the decision result within the effective time, and determines that the executability is lower if the effective time required for meeting the target required by the decision result is longer; the lower the executable degree is, the lower the score of the regulation and control decision module is; C. under the condition that the group comprises more than three regulation and control decision modules, the decision similarity indexes of all the regulation and control decision modules can be calculated, and if the similarity indexes of one decision result and other decision results are low, the score is low; D. obtaining the voting score result of each decision result according to the score obtained in the step B or the accumulation of the scores obtained in the step B and the step C;
and processing preset tasks, transmitting data among groups, monitoring the running state of each group and adjusting the group structure through the central server.
5. The intelligent building indoor air environment maintenance method according to claim 4, wherein the control decision module comprises a BP neural network, the input quantity of the BP neural network is air quality data collected by an air quality sensor in a building space corresponding to the control decision module, and the output quantity of the BP neural network is a control preset value for each air environment maintenance device in a group corresponding to the building space.
6. The intelligent building indoor air environment maintenance method according to claim 4, wherein the number of the regulation decision modules configured in the corresponding building space is determined according to any one or more of the number of air quality sensors in the corresponding building space, the number of air environment maintenance devices, the size of the corresponding building space, and the complexity of the corresponding building space.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022266451A1 (en) * | 2021-06-17 | 2022-12-22 | Research Products Corporation | Whole building air quality control system |
Families Citing this family (1)
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| CN113589729A (en) * | 2021-07-30 | 2021-11-02 | 季伟 | Intelligent building automatic control method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104238510A (en) * | 2014-09-03 | 2014-12-24 | 尚高科技有限公司 | Intelligent building district management system based on Internet of things |
| CN205352448U (en) * | 2015-12-28 | 2016-06-29 | 哈尔滨理工大学 | Distributing type indoor air quality monitoring devices |
| CN107599783A (en) * | 2017-09-28 | 2018-01-19 | 吉林大学 | A kind of environment inside car management system and its control method |
| CN208092003U (en) * | 2018-04-17 | 2018-11-13 | 四川巴斯德环境检测技术有限责任公司 | A kind of distributed room air detecting system based on wireless transmission |
| CN208458194U (en) * | 2018-03-27 | 2019-02-01 | 东莞市宇洁新材料有限公司 | A kind of distributed space air purifying apparatus based on low-power consumption wide area network |
| CN209570832U (en) * | 2019-04-12 | 2019-11-01 | 天津科技大学 | A kind of intelligent building air monitoring system based on Internet of Things |
Family Cites Families (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101145752B (en) * | 2007-08-17 | 2010-06-02 | 国电南京自动化股份有限公司 | Channel switching technology of generator excitation adjusting device |
| US9703665B1 (en) * | 2010-02-19 | 2017-07-11 | Acuity Holdings, Inc. | Data center design process and system |
| CN201845215U (en) * | 2010-05-11 | 2011-05-25 | 深圳绿拓科技有限公司 | Concentrating-distributing type wireless control system, master controller and slave controllers |
| CN102621938A (en) * | 2011-01-28 | 2012-08-01 | 上海新华控制技术(集团)有限公司 | Triple redundancy control system in process control and method thereof |
| US9664702B2 (en) * | 2011-09-25 | 2017-05-30 | Theranos, Inc. | Fluid handling apparatus and configurations |
| CN102752164B (en) * | 2011-10-19 | 2017-12-29 | 新奥特（北京）视频技术有限公司 | A kind of switching method and system of the two-node cluster hot backup of scoring system |
| CN104472023B (en) * | 2011-12-01 | 2018-03-27 | 伊利诺伊大学评议会 | It is designed to undergo the transient state device of programmable transformation |
| CN103036760B (en) * | 2012-12-18 | 2016-01-06 | 迈普通信技术股份有限公司 | The topological convergence method of pile system and member device |
| CN103914354A (en) * | 2012-12-31 | 2014-07-09 | 北京新媒传信科技有限公司 | Method and system for database fault recovery |
| CN105229545A (en) * | 2013-03-14 | 2016-01-06 | 林肯环球股份有限公司 | For creating or change the system and method for sequence of welds |
| CN103838230B (en) * | 2014-03-11 | 2016-04-06 | 南京景曜智能科技有限公司 | The failure switching method of the digital rudder controller control system of a kind of extendible redundance and the number of axle |
| US11728030B2 (en) * | 2014-09-29 | 2023-08-15 | Apple Inc. | Methods of treatment and diagnosis using enhanced patient-physician communication |
| CN205103641U (en) * | 2015-10-08 | 2016-03-23 | 西北师范大学 | Smart home systems based on MSP430 singlechip |
| CN205880552U (en) * | 2016-08-08 | 2017-01-11 | 杭州新湾科技股份有限公司 | But intelligent building control system's redundant controller |
| CN106713056B (en) * | 2017-03-17 | 2019-06-28 | 郑州云海信息技术有限公司 | A kind of method of standby host election switching under distributed type assemblies |
| CN107205217B (en) * | 2017-06-19 | 2020-11-10 | 深圳软通动力信息技术有限公司 | Uninterrupted content pushing method and system based on intelligent sound box scene networking |
| US20190120471A1 (en) * | 2017-10-20 | 2019-04-25 | Essential Products, Inc. | Multi-function wall switch |
| KR101865002B1 (en) * | 2017-10-31 | 2018-06-05 | 김덕원 | automatic control panel box having control and management temperature and humidity |
| AU2018385249B2 (en) * | 2017-12-13 | 2024-02-01 | Medtronic Minimed, Inc. | Methods and systems for continuous glucose monitoring |
| CN108199414B (en) * | 2018-01-29 | 2020-05-19 | 南方电网科学研究院有限责任公司 | Microgrid control method and device, computer equipment and storage medium |
| CN109491238B (en) * | 2018-12-12 | 2022-06-03 | 南京工程学院 | Redundancy method for controller of subway platform door control system |
| CN109683467A (en) * | 2018-12-24 | 2019-04-26 | 浙江欧佩亚海洋工程有限公司 | A kind of Dynamic Positioning Control System voting system and its method |
| CN110278644A (en) * | 2019-06-28 | 2019-09-24 | 山东建筑大学 | A kind of more architectural lighting monitoring methods and system based on edge calculations |
-
2019
- 2019-12-24 CN CN201911351265.5A patent/CN111198545B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104238510A (en) * | 2014-09-03 | 2014-12-24 | 尚高科技有限公司 | Intelligent building district management system based on Internet of things |
| CN205352448U (en) * | 2015-12-28 | 2016-06-29 | 哈尔滨理工大学 | Distributing type indoor air quality monitoring devices |
| CN107599783A (en) * | 2017-09-28 | 2018-01-19 | 吉林大学 | A kind of environment inside car management system and its control method |
| CN208458194U (en) * | 2018-03-27 | 2019-02-01 | 东莞市宇洁新材料有限公司 | A kind of distributed space air purifying apparatus based on low-power consumption wide area network |
| CN208092003U (en) * | 2018-04-17 | 2018-11-13 | 四川巴斯德环境检测技术有限责任公司 | A kind of distributed room air detecting system based on wireless transmission |
| CN209570832U (en) * | 2019-04-12 | 2019-11-01 | 天津科技大学 | A kind of intelligent building air monitoring system based on Internet of Things |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022266451A1 (en) * | 2021-06-17 | 2022-12-22 | Research Products Corporation | Whole building air quality control system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111198545A (en) | 2020-05-26 |
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