Community intelligent building deployment method and system

Abstract

The invention provides a community intelligent building deployment method and system, and relates to the technical field of intelligent buildings, and the method comprises the steps: collecting the building information of a community intelligent building, and the building information represents the related information of the structure and function of the building; determining the correlation degree between the community intelligent buildings according to the building information of the community intelligent buildings; and generating a deployment strategy of the community intelligent buildings based on the association degree between the community intelligent buildings. According to the community intelligent system, the intelligent buildings with higher association degree are deployed at closer positions, so that monitoring and management are facilitated, the robustness of the community intelligent buildings is improved, and the community intelligent system with low cost and higher robustness is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent buildings, and in particular to a method and system for deploying a community intelligent building. Background Art

[0002] The concept of intelligent communities in the biological world is introduced into the field of intelligent buildings, resulting in the concept of community intelligent buildings; community intelligent buildings are a major technological achievement that integrates different disciplines such as artificial intelligence, the Internet, the Internet of Things, big data, and is applied to the field of architecture. Since its proposal, it has gradually attracted people's attention because it can solve various problems existing in the application of traditional centralized architecture to intelligent building control.

[0003] However, there are many equipment systems inside the building space unit, the control information is complicated, and different equipment systems and control loops exist in the same space at the same time, and there is a high degree of coupling between them. Such a centralized architecture has a single point failure risk. The failure of any node may cause the paralysis of the entire system, resulting in poor robustness and stability of such community intelligent buildings built through centralized architecture. Summary of the invention

[0004] The present invention provides a community intelligent building deployment method and system, which are used to solve the defects of poor robustness and stability of community intelligent buildings constructed with centralized architecture in the prior art, and realize a community intelligent building deployment method and system with higher robustness.

[0005] The present invention provides a method for deploying a community intelligent building, comprising: Collecting building information of the community intelligent building, wherein the building information represents relevant information on the structure and function of the building; Determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings; A deployment strategy for the community intelligent buildings is generated based on the correlation between the community intelligent buildings.

[0006] According to a method for deploying a community intelligent building provided by the present invention, before the step of determining the correlation between community intelligent buildings according to the building information of the community intelligent buildings, the method further includes: Classifying the building information of the community intelligent building according to the basic attributes of the community intelligent building to obtain a set of building information of several categories; The building information sets of the several categories are respectively organized into matrix forms.

[0007] According to a cluster intelligent building deployment method provided by the present invention, the step of determining the correlation between cluster intelligent buildings according to the building information of the cluster intelligent buildings specifically includes: Calculate the associated weight set of each type of building information set according to the functional parameters of each type of building information set; According to the building position comparison result of the building information set of the building information and the building information sets of the several categories, the association weight set of each category of building information set is reconstructed to obtain the association weight set of each category of building information after reconstruction; The association weight sets after reconstruction of various types of building information are fused and calculated to obtain the association degree between the community intelligent buildings.

[0008] According to a community intelligent building deployment method provided by the present invention, the step of calculating the association weight set of each type of building information set according to the functional parameters of each type of building information set specifically includes: Calculate the function weight set of each building according to the function parameters of each type of building information set; Calculating the association weights of the multiple buildings according to the functional weight set of each building; The association weight set of each type of building information set is obtained according to the association weights of the multiple buildings.

[0009] According to a community intelligent building deployment method provided by the present invention, the step of generating a community intelligent building deployment strategy based on the association between the community intelligent buildings specifically includes: Determine the priority according to the weight value of each weight in the association degree between the community intelligent buildings; A deployment strategy for the community intelligent building is generated based on the priority.

[0010] The present invention also provides a community intelligent building deployment system, comprising: A collection module, used to collect building information of the community intelligent building, wherein the building information represents relevant information on the structure and function of the building; A calculation module, used for determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings; A deployment module is used to generate a deployment strategy for the community intelligent buildings based on the association between the community intelligent buildings.

[0011] A community intelligent building deployment system provided by the present invention also includes: A preprocessing module, used for classifying the building information of the community intelligent building according to the basic attributes of the community intelligent building, and obtaining a set of building information of several categories; The building information sets of the several categories are respectively organized into matrix forms.

[0012] A community intelligent building deployment system provided according to the present invention further includes an early warning module, which is used to update the association between the community intelligent buildings according to the monitoring results of the community intelligent buildings.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any of the above-mentioned community intelligent building deployment methods is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for deploying a community intelligent building as described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned community intelligent building deployment methods.

[0016] The community intelligent building deployment method and system provided by the present invention calculates the correlation between intelligent buildings under different classification conditions, generates a deployment strategy for the community intelligent buildings based on the correlation between the community intelligent buildings, and deploys multiple intelligent buildings with higher correlation at closer locations for easy monitoring and management, thereby improving the robustness of the community intelligent buildings and realizing a low-cost and highly robust community intelligent system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the method for deploying a community intelligent building provided by the present invention; Figure 2 This is one of the structural schematic diagrams of the cluster intelligent building deployment device provided by the present invention; Figure 3 This is the second structural schematic diagram of the cluster intelligent building deployment device provided by the present invention; Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Combine the following Figure 1 The community intelligent building deployment method of the present invention is introduced, such as Figure 1 As shown, including: Step 101, collecting building information of the community intelligent building, wherein the building information represents relevant information on the structure and function of the building; The building information of each building in the cluster intelligent building to be deployed is collected in advance.

[0021] Optionally, the community intelligent building to be deployed may be a community intelligent building that has not yet been deployed.

[0022] Optionally, the community intelligent building to be deployed may be a community intelligent building that has been partially deployed, by further deploying multiple intelligent buildings to form a new community intelligent building.

[0023] Building information represents relevant information about the structure and function of a building. Optionally, the building information includes at least the name of the building project, scale, nature, building function (purpose), earthquake resistance level, building height, construction age, building area, structural form, air-conditioning area, heating area, building air-conditioning system form, and building heating system form.

[0024] The basic functions and attributes of each building in the community intelligent building can be obtained through building information, so as to generate the deployment strategy of the community intelligent building based on this.

[0025] Step 102, determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings; It is understandable that, since the building information contains building-related information of different categories, by classifying the building information, the correlation between multiple buildings of each category can be determined, and then the inter-class correlation of buildings of multiple categories can be determined as the correlation between community intelligent buildings.

[0026] Therefore, the association degree between the community intelligent buildings includes a set of association degree weights between multiple different categories of intelligent building clusters.

[0027] Step 103: generating a deployment strategy for the community intelligent buildings based on the associations between the community intelligent buildings.

[0028] When deploying a community of intelligent buildings, multiple intelligent buildings to be deployed are selected, and then the corresponding correlation weights are selected from the correlations between the community of intelligent buildings based on the categories of concern. The priority is determined according to the weight, and a deployment strategy is generated based on the priority level to give priority to meeting the resource requirements of high-priority buildings and ensure the stable operation of key functions and services.

[0029] The present invention calculates the correlation between intelligent buildings under different classification conditions, generates a deployment strategy for community intelligent buildings based on the correlation between community intelligent buildings, deploys multiple intelligent buildings with higher correlation at closer locations for easy monitoring and management, improves the robustness of community intelligent buildings, and realizes a low-cost and highly robust community intelligent system.

[0030] In the community intelligent building deployment method of the present invention, before the step of determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings, the method further includes: Classifying the building information of the community intelligent building according to the basic attributes of the community intelligent building to obtain a set of building information of several categories; The building information sets of the several categories are respectively organized into matrix forms.

[0031] Collect the building information of the community intelligent building in advance to obtain the building information collection , ,in, N Indicates the total number of buildings in the community, set Any element in express. Indicates j A subset of building information for each building, ,in, M Indicates j The number of building information of a building, set Any element of express, Indicates j The building's i Building information.

[0032] The collected building information is processed into a matrix form as shown below: ; Furthermore, the collected building information is classified according to the basic attributes of the community intelligent building.

[0033] Optionally, the buildings are classified according to their use functions, which include residential buildings, public buildings, industrial buildings, and agricultural buildings. The classified set expression is: ; In the formula, Represents a subset of residential buildings, available represents any element in the residential building subset, , Indicates the number of a A collection of information about a building.

[0034] Represents a public building subset, available represents any element in the public buildings sub-collection, , Indicates the first b A collection of information about a building.

[0035] Represents a subset of industrial buildings, available Represents any element in the Industrial Buildings sub-collection, , Indicates the first c A collection of information about a building.

[0036] Represents a subset of agricultural buildings, available represents any element in the Agricultural Buildings sub-collection, , Indicates the number of d A collection of information about a building.

[0037] and .

[0038] Optionally, according to the classification of construction projects, construction projects include type I, type II, type III, type IV and type V buildings, and the classified set expression is obtained: ; In the formula, Represents a subset of buildings, available Any element in the building subset representing a type of building project, , Indicates the first x A collection of information about a building.

[0039] Represents a subset of the second type of buildings, available Any element in the building subset representing a second-class building project, , Indicates that in the second category of construction engineering subset yA collection of information about a building.

[0040] Represents three types of building subsets, available Any element in the building subset representing the three types of building projects, , Indicates that among the three types of construction engineering subsets, z A collection of information about a building.

[0041] Represents four types of building subsets, available Any element in the building subset representing the four types of construction projects, , Indicates that among the four types of construction engineering subsets, e A collection of information about a building.

[0042] Represents five types of building subsets, available Any element in the building subset representing the five types of construction projects, , Indicates that among the five types of construction engineering subsets, f A collection of information about a building.

[0043] and .

[0044] Optionally, the building scale is classified according to the building scale, which includes mass buildings and large buildings; the mass buildings refer to buildings that are large and closely related to people's lives, such as residences, schools, shops, hospitals, small and medium-sized office buildings, etc.; the large buildings refer to buildings that are large in scale, cost a lot, and have a great impact, such as large railway stations, large gymnasiums, museums, and conference halls. The expression after classification is obtained: ; In the formula, Represents a large number of building subsets, available Any element in the Buildings subset representing a large number of construction projects, , Indicates the number of p A collection of information about a building.

[0045] Represents a large sub-collection of buildings, available Any element in the Buildings subset that represents a large-scale construction project, , Indicates the first q A collection of information about a building.

[0046] and .

[0047] Optionally, the building is classified according to its fire resistance level, including first-class fire resistance building, second-class fire resistance building, third-class fire resistance building and fourth-class fire resistance building, to obtain the classification expression .

[0048] Optionally, according to the seismic fortification classification of the building, including Class A (special fortification class), Class B (key fortification class), Class C (standard fortification class) and Class D (moderate fortification class), the classification expression is obtained .

[0049] It should be noted that the above classification set is only a reorganization of the collected building information, and all elements in it are taken from the building information set BIN In the BIN Re-sort the positions of the elements in .

[0050] At the same time, in order to facilitate the subsequent calculation of the correlation, the building information is also normalized when it is organized into a matrix form.

[0051] The present invention obtains building information by collecting information of community intelligent buildings, and represents the collected building information in a matrix form that is easy to process. The building information represented by the matrix is ​​classified and preprocessed using a variety of classification categories to obtain multiple building information matrix forms to provide data basis for subsequent correlation calculations between buildings and optimized strategic deployment, thereby further obtaining building correlation information at a lower cost and more easily.

[0052] In the community intelligent building deployment method of the present invention, the step of determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings specifically includes: Calculate the associated weight set of each type of building information set according to the functional parameters of each type of building information set; For each type of building information set, calculate the associated weight set of each type of building information set .

[0053] Alternatively, taking the category of building use as an example, ,in, Respectively represent the weight set of functional building classification, the associated weight set of any two functional building classifications, ..., any N A collection of association weights for each functional building classification.

[0054] Furthermore, for building information collection classified according to construction projects , and get the corresponding association weight set ; For building information collection classified by building scale , and get the corresponding association weight set ; For information collection of buildings classified by fire resistance level , and get the corresponding association weight set ; For building information collection classified according to seismic fortification level , and get the corresponding association weight set .

[0055] According to the building position comparison result of the building information set of the building information and the building information sets of the several categories, the association weight set of each category of building information set is reconstructed to obtain the association weight set of each category of building information after reconstruction; The building information set of building information is a set of building information sorted out based on building information BIN .

[0056] On this basis, according to each type of building information collection , , , and , and the building information collection of building information Compare all building locations in the The location coordinates of each building in the above are used as references to find the corresponding positions in each type of building information set mentioned above, and further, according to the corresponding reference positions, the associated weight sets are rearranged according to the reference positions into the associated weight sets after the preparation, and reconstructed to obtain the reconstructed associated weight sets: , , , and , which provides a location basis for calculating the correlation between buildings and facilitates searching.

[0057] In a specific embodiment: For each type of building information set (e.g. correspond ), first read the location coordinates of each building. Find the The building corresponding to the building position in . Assume that Buildings in i The position coordinates are Coord i , then in Find the closest location Coord i Architecture j , denoted as Coord jOnce a matching pair of buildings is found ( i , j ), then Corresponding i The weight value is adjusted to j Repeat this process until All buildings in the The position in.

[0058] In the same way, , , , Processing is performed to obtain the associated weight set after location reference , , , , For each category of reconstruction set , retaining the spatial position distribution of its associated weights in physical space, and taking the rearranged weight set as a new set to form a reconstructed associated weight set aligned with the building position in BIN During the rearrangement process, it is necessary to ensure that the accuracy of the physical space of the building is taken into account when matching the location coordinates. That is, during the matching process, it is necessary to use a pre-set distance threshold to determine whether two buildings are "adjacent" or "the same". Finally, each type of building information (such as , , , , ) The reconstructed weight set is The positions of the buildings in the image are integrated to form a complete set of reconstructed association weights.

[0059] The association weight sets after reconstruction of various types of building information are fused and calculated to obtain the association degree between the community intelligent buildings.

[0060] For the reconstructed association weight , , , and , the correlation degree Q between the community intelligent buildings is calculated: ; In the formula, Indicates y Classification Building Collection x The number of elements in the association weight set, Indicates y Classification Building Collection xA set of associated weights.

[0061] In the community intelligent building deployment method of the present invention, the step of calculating the association weight set of each type of building information set according to the functional parameters of each type of building information set specifically includes: Calculate the function weight set of each building according to the function parameters of each type of building information set; Taking the building information set corresponding to the category of building use function as an example, the association weight set corresponding to the building information set of this category is calculated.

[0062] First, calculate the functional weight set of each building , where the set Any element in express, Indicates j The functional weight information corresponding to each building, , defined as follows: ; In the formula, Indicates j The number of residents in a building, Indicates j The size of the building, Indicates j The utilization rate of a building, Indicates j The development trend parameters of each building, Indicates j Other influencing parameters of a building, Indicates j The number of mobile people in a building, Indicates j Profitability parameters of a building, Indicates j The capacity of a building.

[0063] It can be understood that the corresponding weight is calculated according to the functional parameters of the set of each category.

[0064] Optionally, corresponding to the building information set of the category of building use function, the functional parameters include the number of residents, size, utilization rate, development trend parameters, number of mobile people, profit parameters, capacity and other influencing parameters of the building.

[0065] Optionally, corresponding to the building information set of the category of construction projects, the functional parameters include project type, construction unit and construction quality.

[0066] Optionally, corresponding to the building information set of the building scale category, the functional parameters include building area, building height, floor area ratio, building volume and number of floors.

[0067] Optionally, corresponding to the building information set according to the category of fire resistance level, the functional parameters include fire resistance level, fire resistance material, fire partition and fire protection setting.

[0068] Optionally, corresponding to the building information set of the category of seismic fortification level, the functional parameters include seismic fortification category, seismic design standard, seismic measures and seismic performance evaluation.

[0069] Calculating the association weights of the multiple buildings according to the functional weight set of each building; On this basis, taking the building information set corresponding to the category of building use function as an example, the association weights of any two buildings in the building information set of this category are calculated according to the following formula: ; In the formula, Indicates r , t The functional association weight value of each building, , Respectively represent r , t A set of functional parameters of a building, Indicates r , t The number of intersections of the function parameter sets of the buildings, , Respectively represent the functional weight parameters of each building; It is r The size of the building, yes t The size of the building, is the total size of all buildings, It is r and t The size of the union of each set of building function parameters represents the total number of two building function parameters; is the total size of all building function parameter sets, , is the adjustment factor.

[0070] Furthermore, taking the building information set corresponding to the category of building use function as an example, the association weights of any three buildings in the building information set of this category are calculated according to the following formula: ; In the formula, Indicates r ,t and e The functional association weight value of each building, , and Respectively represent r , t and e A set of functional parameters of a building, Indicates r , t and e The number of intersections of the function parameter sets of the buildings, , and Indicates r , t and e Functional weight parameters of buildings, L= , which means the value of taking any two of the three buildings. represents the attribute added value of the sth associated building obtained through multiple experiments, such as building characteristics and historical cooperation relationships, Represents the association weight value between any two buildings.

[0071] Optionally, the attribute additional value is determined according to the category of the building information set.

[0072] According to the above method, the associated weight values ​​of any multiple buildings in the building information set of each category can be determined.

[0073] The association weight set of each type of building information set is obtained according to the association weights of the multiple buildings.

[0074] According to the above method, the associated weight set of the building information set corresponding to the category of building use function can be obtained , .in, Respectively represent the weight set of a single building, the associated weight set of any two buildings, ..., any N The associated weights of each building.

[0075] Similarly, for each category of building information set, its corresponding association weight set can be calculated and integrated in the same way.

[0076] Optionally, corresponding to the building information set of the construction project category, obtain its corresponding association weight set .

[0077] Optionally, the building information set corresponding to the building scale category is obtained by obtaining the corresponding association weight set: .

[0078] Optionally, corresponding to the building information set classified according to the fire resistance level, the corresponding association weight set is obtained: .

[0079] Optionally, the building information set corresponding to the category of seismic fortification level is obtained to obtain its corresponding association weight set .

[0080] In the community intelligent building deployment method of the present invention, the step of generating a deployment strategy for the community intelligent building based on the correlation between the community intelligent buildings specifically includes: Determine the priority according to the weight value of each weight in the association degree between the community intelligent buildings; A deployment strategy for the community intelligent building is generated based on the priority.

[0081] In a feasible implementation, according to the composition of the community intelligent buildings to be deployed or actually deployed, the association weight set between each community intelligent building is calculated. Q The corresponding association weights are selected from the set to form a new association weight set of the community intelligent building. The corresponding association weights are the association weights that are concerned when deploying, building or maintaining the community intelligent building.

[0082] The priority is set according to the weight value of each weight in the associated weight set of the new community intelligent building, and then the deployment is optimized according to the priority and the information of each building to obtain the deployment strategy of the community intelligent building.

[0083] Specifically, deployment strategies may include optimizing resource allocation strategies, giving priority to meeting resource demands between high-priority buildings, and ensuring the stable operation of key functions and services; optimizing network layout, designing a reasonable network topology, optimizing network connections, and ensuring efficient and reliable data communication between highly correlated buildings; optimizing energy management, optimizing energy management strategies based on energy consumption data and correlation between buildings, achieving energy sharing and complementarity, reducing overall energy consumption, and improving energy utilization efficiency; equipment and service scheduling, optimizing equipment and service scheduling inside and outside buildings, and ensuring that equipment and service demands of high-priority buildings are given priority; and emergency response strategies, formulating emergency response strategies to ensure that in the event of emergencies or system failures, the safety and stable operation of highly correlated buildings are given priority.

[0084] In a specific embodiment: Assume that there is a smart building community, which contains four buildings: A, B, C and D. The information of each building, such as use function, construction project, building scale, fire resistance level and earthquake fortification, has been preprocessed and the corresponding association weight set has been generated. Assume that the reconstructed association weight sets are: , , , , The values ​​in these collections are as follows: (Use function): AB: 0.8, AC: 0.5, AD: 0.4, BC: 0.7, BD: 0.6, CD: 0.9; (Construction Engineering): AB: 0.9, AC: 0.3, AD: 0.7, BC: 0.5, BD: 0.8, CD: 0.6; (Building size): AB: 0.6, AC: 0.4, AD: 0.8, BC: 0.7, BD: 0.5, CD: 0.9; (Fire resistance level): AB: 0.7, AC: 0.6, AD: 0.5, BC: 0.8, BD: 0.9, CD: 0.4; (Seismic fortification): AB: 0.5, AC: 0.7, AD: 0.6, BC: 0.9, BD: 0.4, CD: 0.8; Calculate the association weight set between the cluster intelligent buildings Q : ; Calculate the overall association weight Q(MN) between the Mth building and the Nth building.

[0085] For example, to calculate the association weight Q(AB) of building AB: ; Similarly, calculate the association weights of other building pairs: Q(AC): ; Q(AD): ; Q(BC): ; Q(BD): ; Q(CD): ; According to the calculated association weight set Q, the following optimization deployment strategy is formulated: 1. Resource allocation strategy: Prioritize satisfying resource demands between BC and CD. These two groups of buildings have the highest correlation weight of 0.72, indicating that the interaction and interdependence between them are the strongest, ensuring that resources are adequately supplied between these buildings to avoid interruptions in critical functions.

[0086] The next priority is to allocate resources to BD and AB, with weights of 0.64 and 0.7 respectively, to ensure stable operation among these buildings.

[0087] 2. Network layout optimization: Design a dedicated high-speed network channel between BC and CD buildings to ensure efficient data transmission. Since BC and CD have the highest degree of correlation, communication reliability can be guaranteed by adding redundant connections and bandwidth.

[0088] The connection weights of AD and BD are relatively high, and through reasonable topology design, secondary but reliable network paths can be established between these buildings.

[0089] 3. Energy management optimization: Based on the energy consumption data and correlation between buildings, BC and CD can design a shared energy management system to improve overall energy efficiency through energy storage and transfer.

[0090] For buildings in AB and AD, complementary energy management strategies can be designed to support each other during high load periods and reduce the pressure on independent energy supply.

[0091] 4. Equipment and service scheduling: Equipment and service needs between BC and CD should be prioritized, such as emergency maintenance services, equipment updates, etc., to ensure normal operations between the two buildings.

[0092] For buildings AB and BD, an alternative plan is adopted to provide scheduling support for these buildings after the main demand is met.

[0093] 5. Emergency response strategy: In emergency response, priority should be given to ensuring the safety and operation of BC and CD buildings, and targeted emergency response measures should be formulated to ensure that these buildings can be quickly restored or continue to operate in the event of an emergency.

[0094] For lower priority buildings (such as AD and AB), secondary emergency response plans are designed to ensure their safety under limited resources.

[0095] Through the above strategies, when resources and conditions are limited, priority can be given to ensuring the stable operation of key buildings and optimizing the management and operation efficiency of the entire intelligent building community.

[0096] The present invention gradually calculates the building correlation under each classification through the collected classified building information, and further performs fusion calculation according to the associated weight set of each classified building information set to obtain the correlation between each community intelligent building, thereby improving the robustness of the community intelligent building. Then, the community buildings are optimized and deployed according to the correlation and building information of each intelligent building, further providing a reference basis for the optimized management of the community intelligent building, and realizing a low-cost and highly robust community intelligent system.

[0097] The community intelligent building deployment system provided by the present invention is described below. The community intelligent building deployment system described below and the community intelligent building deployment method described above can be referred to each other.

[0098] like Figure 2 As shown, the community intelligent building deployment system includes a collection module 201, a calculation module 202 and a deployment module 203: A collection module 201 is used to collect building information of a community intelligent building, wherein the building information represents relevant information on the structure and function of the building; The building information of each building in the cluster intelligent building to be deployed is collected in advance.

[0099] Optionally, the community intelligent building to be deployed may be a community intelligent building that has not yet been deployed.

[0100] Optionally, the community intelligent building to be deployed may be a community intelligent building that has been partially deployed, by further deploying multiple intelligent buildings to form a new community intelligent building.

[0101] Building information represents relevant information about the structure and function of a building. Optionally, the building information includes at least the name of the building project, scale, nature, building function (purpose), earthquake resistance level, building height, construction age, building area, structural form, air-conditioning area, heating area, building air-conditioning system form, and building heating system form.

[0102] The basic functions and attributes of each building in the community intelligent building can be obtained through building information, so as to generate the deployment strategy of the community intelligent building based on this.

[0103] A calculation module 202, configured to determine the correlation between the community intelligent buildings according to the building information of the community intelligent buildings; It is understandable that, since the building information contains building-related information of different categories, by classifying the building information, the correlation between multiple buildings of each category can be determined, and then the inter-class correlation of buildings of multiple categories can be determined as the correlation between community intelligent buildings.

[0104] Therefore, the association degree between the community intelligent buildings includes a set of association degree weights between multiple different categories of intelligent building clusters.

[0105] The deployment module 203 is used to generate a deployment strategy for the community intelligent buildings based on the association between the community intelligent buildings.

[0106] When deploying a community of intelligent buildings, multiple intelligent buildings to be deployed are selected, and then the corresponding correlation weights are selected from the correlations between the community of intelligent buildings based on the categories of concern. The priority is determined according to the weight, and a deployment strategy is generated based on the priority level to give priority to meeting the resource requirements of high-priority buildings and ensure the stable operation of key functions and services.

[0107] The present invention calculates the correlation between intelligent buildings under different classification conditions, generates a deployment strategy for community intelligent buildings based on the correlation between community intelligent buildings, deploys multiple intelligent buildings with higher correlation at closer locations for easy monitoring and management, improves the robustness of community intelligent buildings, and realizes a low-cost and highly robust community intelligent system.

[0108] The community intelligent building deployment system of the present invention also includes: A preprocessing module, used for classifying the building information of the community intelligent building according to the basic attributes of the community intelligent building, and obtaining a set of building information of several categories; The building information sets of the several categories are respectively organized into matrix forms.

[0109] The preprocessing module receives the building information sent by the acquisition module and organizes it into a matrix to obtain a building information set. , ,in, N Indicates the total number of buildings in the community, set Any element in express. Indicates j A subset of building information for each building, ,in, M Indicates j The number of building information of a building, set Any element of express, Indicates j The building's i Building information.

[0110] The collected building information is processed into a matrix form as shown below: ; Furthermore, the collected building information is classified according to the basic attributes of the community intelligent building.

[0111] Optionally, the buildings are classified according to their use functions, which include residential buildings, public buildings, industrial buildings, and agricultural buildings. The classified set expression is: ; Optionally, according to the classification of construction projects, construction projects include type I, type II, type III, type IV and type V buildings, and the classified set expression is obtained: ; Optionally, the building scale is classified according to the building scale, which includes mass buildings and large buildings; the mass buildings refer to buildings that are large and closely related to people's lives, such as residences, schools, shops, hospitals, small and medium-sized office buildings, etc.; the large buildings refer to buildings that are large in scale, cost a lot, and have a great impact, such as large railway stations, large gymnasiums, museums, and conference halls. The expression after classification is obtained: ; Optionally, the building is classified according to its fire resistance level, including first-class fire resistance building, second-class fire resistance building, third-class fire resistance building and fourth-class fire resistance building, to obtain the classification expression .

[0112] Optionally, according to the seismic fortification classification of the building, including Class A (special fortification class), Class B (key fortification class), Class C (standard fortification class) and Class D (moderate fortification class), the classification expression is obtained .

[0113] It should be noted that the above classification set is only a reorganization of the collected building information, and all elements in it are taken from the building information set BIN In the BIN Re-sort the positions of the elements in .

[0114] At the same time, in order to facilitate the subsequent calculation of the correlation, the building information is also normalized when it is organized into a matrix form.

[0115] The community intelligent building deployment system of the present invention further includes an early warning module, which is used to update the correlation between the community intelligent buildings according to the monitoring results of the community intelligent buildings.

[0116] Furthermore, the community intelligent building deployment system also includes an early warning module for updating the correlation between community intelligent buildings according to the monitoring results of the community intelligent buildings.

[0117] In a specific embodiment, Figure 3As shown, the community intelligent building deployment system includes a collection module 301 for collecting building information of each building in the community intelligent building. The collection module 301 sends the collected building information to the building management module 306 for management, and sends it to the preprocessing module 302, especially classifying and arranging the building information into a matrix form.

[0118] The preprocessing module 302 sends the processed information to the calculation module 303, and the calculation module 303 calculates the correlation between the cluster intelligent buildings. Q , providing parameter basis for optimizing allocation processing.

[0119] The calculation module 303 calculates the correlation degree Q The information is sent to the deployment module 304, and the cluster intelligent building is optimally deployed according to the pre-processed building information and the correlation between the buildings, so that the status and utilization rate of all equipment in the cluster building are optimal.

[0120] It also includes an execution module 305, which is used to receive the deployment strategy generated by the deployment module, and to issue execution commands to each building, and to execute the issued commands to complete the execution of the commands.

[0121] It also includes an early warning module 307, which is used to centrally monitor and warn the equipment in the community intelligent building and the power equipment, air-conditioning equipment, cold and heat source equipment, fire prevention equipment, and anti-theft equipment in the community intelligent building of each module, so as to ensure the smooth operation of the community intelligent building system.

[0122] The early warning module 307 monitors the collaboration between the modules of the community intelligent building. Specifically, the monitoring parameters of each building are used to perform routine monitoring and alarm according to the thresholds set in advance according to the empirical method. At the same time, a prediction alarm is provided for the optimized deployment strategy to formulate a more optimized deployment strategy. The specific prediction process is as follows: Through the survey, the satisfaction of people in the community buildings with each building, building information, development plans and other influencing elements of community intelligent buildings will be obtained to form an input set. The input set will be deeply learned through the existing deep learning neural network to obtain the development trend of community intelligent buildings. It will be further compared with the priority of each intelligent building in the formulated optimization deployment. If they are consistent, the community intelligent building system will operate normally. If the difference is large, an early warning will be issued. At the same time, the associated weight set will be recalculated according to the change of development trend, and the community intelligent building will be further optimized and deployed according to the associated weight set to ensure the smooth operation of the community intelligent building system.

[0123] In addition, a database 308 is provided for storing various data generated during the collection, monitoring and optimization processes.

[0124] The present invention provides a basis for the system to obtain a more optimized deployment strategy through conventional monitoring and alarm of community intelligent buildings and early warning of the optimized deployment module by constructing an alarm prediction model, further obtaining a lower-cost community intelligent building system, and at the same time, a more optimized deployment strategy is generated, thereby improving the robustness of the community intelligent building system.

[0125] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the community intelligent building deployment method, which includes: collecting the building information of the community intelligent building, the building information represents the relevant information of the building in structure and function; determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings; and generating the deployment strategy of the community intelligent buildings based on the correlation between the community intelligent buildings.

[0126] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0127] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the community intelligent building deployment method provided by the above methods. The method includes: collecting building information of community intelligent buildings, wherein the building information represents relevant information about the structure and function of the building; determining the correlation between community intelligent buildings based on the building information of the community intelligent buildings; and generating a deployment strategy for the community intelligent buildings based on the correlation between the community intelligent buildings.

[0128] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the community intelligent building deployment method provided by the above-mentioned methods. The method includes: collecting building information of community intelligent buildings, wherein the building information represents relevant information of the building in structure and function; determining the correlation between community intelligent buildings based on the building information of the community intelligent buildings; and generating a deployment strategy for the community intelligent buildings based on the correlation between the community intelligent buildings.

[0129] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for deploying a cluster intelligent building, characterized in that: include: Collecting building information of the community intelligent building, wherein the building information represents relevant information on the structure and function of the building; Determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings; A deployment strategy for the community intelligent buildings is generated based on the correlation between the community intelligent buildings.

2. The method for deploying a cluster intelligent building according to claim 1, characterized in that: Before the step of determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings, the method further includes: Classifying the building information of the community intelligent building according to the basic attributes of the community intelligent building to obtain a set of building information of several categories; The building information sets of the several categories are respectively organized into matrix forms.

3. The method for deploying a cluster intelligent building according to claim 2, characterized in that: The step of determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings specifically includes: Calculate the associated weight set of each type of building information set according to the functional parameters of each type of building information set; According to the building position comparison result of the building information set of the building information and the building information sets of the several categories, the association weight set of each category of building information set is reconstructed to obtain the association weight set of each category of building information after reconstruction; The association weight sets after reconstruction of various types of building information are fused and calculated to obtain the association degree between the community intelligent buildings.

4. The method for deploying a cluster intelligent building according to claim 3, characterized in that: The step of calculating the associated weight set of each type of building information set according to the functional parameters of each type of building information set specifically includes: Calculate the function weight set of each building according to the function parameters of each type of building information set; Calculating the association weights of the multiple buildings according to the functional weight set of each building; The association weight set of each type of building information set is obtained according to the association weights of the multiple buildings.

5. The method for deploying a community intelligent building according to any one of claims 1 to 4, characterized in that: The step of generating a deployment strategy for the community intelligent buildings based on the association between the community intelligent buildings specifically includes: Determine the priority according to the weight value of each weight in the association degree between the community intelligent buildings; A deployment strategy for the community intelligent building is generated based on the priority.

6. A community intelligent building deployment system, characterized in that: include: A collection module, used to collect building information of the community intelligent building, wherein the building information represents relevant information on the structure and function of the building; A calculation module, used for determining the correlation between the community intelligent buildings according to the building information of the community intelligent buildings; A deployment module is used to generate a deployment strategy for the community intelligent buildings based on the association between the community intelligent buildings.

7. The community intelligent building deployment system according to claim 6, characterized in that: Also includes: A preprocessing module, used for classifying the building information of the community intelligent building according to the basic attributes of the community intelligent building, and obtaining a set of building information of several categories; The building information sets of the several categories are respectively organized into matrix forms.

8. The community intelligent building deployment system according to claim 6, characterized in that: It also includes an early warning module, which is used to update the correlation between the community intelligent buildings according to the monitoring results of the community intelligent buildings.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the community intelligent building deployment method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the community intelligent building deployment method as described in any one of claims 1 to 6 is implemented.

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