CN111245912B

CN111245912B - Intelligent building information monitoring method and device, server and intelligent building system

Info

Publication number: CN111245912B
Application number: CN202010007355.9A
Authority: CN
Inventors: 孟小峰
Original assignee: Shanghai Fast Change Name Business Enterprise Development Co Ltd
Current assignee: Shanghai Lanzi Network Technology Co.,Ltd.
Priority date: 2020-01-04
Filing date: 2020-01-04
Publication date: 2020-11-27
Anticipated expiration: 2040-01-04
Also published as: CN112215273A; CN112200233A; CN111245912A

Abstract

The invention provides an intelligent building information monitoring method, an intelligent building information monitoring device, a server and an intelligent building system. Therefore, the method and the system aim at monitoring the acquisition behaviors of the linkage acquisition process of the building information acquisition nodes, so that whether the issued acquisition tasks can achieve the expected and reasonable effect in the actual linkage acquisition process or not can be conveniently determined subsequently, and the method and the system can aim at the acquisition tasks through characteristic correlation information among the characteristics of the acquisition behaviors and improve the reliability of the linkage acquisition process.

Description

Intelligent building information monitoring method and device, server and intelligent building system

Technical Field

The invention relates to the technical field of intelligent buildings, in particular to an intelligent building information monitoring method, an intelligent building information monitoring device, a server and an intelligent building system.

Background

In the intelligent building monitoring process, intelligent linkage information acquisition aiming at the same monitoring position can be realized through a plurality of building information acquisition nodes, so that the functions of the plurality of building information acquisition nodes are fully exerted, and the monitoring efficiency and the monitoring accuracy are improved. However, in the existing scheme, the collection task is usually only manually issued to the building information collection node associated with each collection task, monitoring of the linkage collection process for the building information collection node is lacked, and it is difficult to determine whether the issued collection task can achieve the expected and reasonable effect in the actual linkage collection process, so that the collection task cannot be adjusted in a targeted manner, and the linkage collection process has great uncertainty.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, the present invention provides an intelligent building information monitoring method, an intelligent building information monitoring device, a server and an intelligent building system, so as to solve or improve the above problems.

In a first aspect, the present invention provides an intelligent building information monitoring method, applied to an intelligent building system, where the intelligent building system includes a server and a building information collection node in communication connection with the server, and the method includes:

the server respectively acquires corresponding building information acquisition behaviors from at least two building information acquisition nodes which are at the same acquisition position and are associated with set acquisition tasks, wherein the associated acquisition tasks refer to the fact that logical association exists in the acquisition process of a target information acquisition object;

the server respectively extracts the characteristics of building information acquisition behaviors corresponding to any two building information acquisition nodes to obtain corresponding acquisition behavior characteristics;

the server performs characteristic relevance analysis on any two extracted acquisition behavior characteristics to obtain characteristic relevance information between any two acquisition behavior characteristics;

the server determines task adjustment strategies of the acquisition tasks according to feature association information between any two acquisition behavior features, and respectively sends the task adjustment strategies to the at least two corresponding building information acquisition nodes;

and the building information acquisition node adjusts the acquisition task according to the task adjustment strategy.

In a possible design of the first aspect, the step of obtaining corresponding building information collection behaviors from at least two building information collection nodes associated with a same collection position and a set collection task respectively includes:

acquiring integration characteristic information of corresponding acquisition process record information from at least two building information acquisition nodes which are at the same acquisition position and are associated with a set acquisition task respectively, wherein the integration characteristic information is used for representing the acquisition process change condition of the acquisition process record information in an integration time period, and the acquisition process change condition comprises one or more combinations of acquisition azimuth change condition, acquisition frequency change condition and acquisition time period change condition;

inputting the integrated characteristic information into a second characteristic expression matrix of the behavior conversion matrix which is updated last time to obtain a plurality of first behavior prediction units and a second prediction behavior corresponding to each first behavior prediction unit; the behavior transformation matrix comprises a first characteristic expression matrix and a second characteristic expression matrix, the first characteristic expression matrix is used for extracting integrated characteristic information of the collected process record information, the second characteristic expression matrix is used for generating a plurality of first behavior prediction units of the collected process record information according to the integrated characteristic information and generating second prediction behaviors for each first behavior prediction unit;

according to the second prediction behaviors, selecting a plurality of behavior prediction units from the plurality of first behavior prediction units and first prediction behaviors corresponding to the behavior prediction units, wherein the first prediction behavior corresponding to each behavior prediction unit is obtained by analyzing the behavior prediction unit based on a behavior transformation matrix updated last time, the acquisition process record information comprises at least one acquisition process node, one acquisition process node corresponds to at least one behavior prediction unit, and the acquisition process record information is added with behavior information of the behavior unit corresponding to each acquisition process node;

selecting at least one target behavior prediction unit from the multiple behavior prediction units according to the first predicted behavior to form a target behavior prediction unit group, and determining behavior extraction information corresponding to the behavior transformation matrix according to a coverage range value between each target behavior prediction unit in the target behavior prediction unit group and a behavior unit of each acquisition process node in the acquisition process record information, wherein the behavior extraction information comprises behavior information of the target behavior prediction unit corresponding to which the coverage range value is greater than a set range value;

detecting initial characteristic information of a target acquisition behavior node from initial acquisition behaviors of the behavior extraction information according to behavior extraction information corresponding to the behavior conversion matrix;

taking the initial acquisition behavior as a previous acquisition behavior and the initial characteristic information as previous characteristic information, and performing characteristic conversion on a next acquisition behavior of the previous acquisition behavior according to a characteristic conversion relation between the previous characteristic information and a target acquisition behavior node strategy to obtain acquisition characteristic information in the next acquisition behavior, wherein the target acquisition behavior node strategy is used for representing preset characteristic information of a preset target acquisition behavior node;

detecting the collected characteristic information in the latter collecting behavior to obtain first characteristic information of a target collecting behavior node in the latter collecting behavior;

taking the latter collection behavior as a former collection behavior and the first characteristic information of a target collection behavior node in the latter collection behavior as former characteristic information, returning to the step of performing characteristic conversion on the latter collection behavior of the former collection behavior according to the characteristic conversion relation between the former characteristic information and a target collection behavior node strategy to obtain the collection characteristic information in the latter collection behavior, and performing iterative processing until the first characteristic information of the target collection behavior node in the last collection behavior of the behavior extraction information is obtained;

taking the last acquisition behavior as the previous acquisition behavior of the initial acquisition behavior, and determining the first characteristic information of the final target acquisition behavior node in the initial acquisition behavior by referring to the first characteristic information of the target acquisition behavior node in the last acquisition behavior;

sequentially selecting the currently processed acquisition behaviors in the behavior extraction information according to a set characteristic sequence, wherein the set characteristic sequence is a time sequence, a null sequence or a frequency sequence, the time sequence is used for representing the sequence by taking time as a sequence, the null sequence is used for representing the sequence by taking a space direction as a sequence, and the frequency sequence is used for representing the sequence by taking frequency as a sequence;

determining a relative acquisition behavior from acquisition behaviors in which a set feature order in the behavior extraction information is located before a current processing acquisition behavior, wherein the relative acquisition behavior is an acquisition behavior associated with the set feature order corresponding to the current processing acquisition behavior;

acquiring first characteristic information of a target acquisition behavior node in the relative acquisition behavior, wherein the first characteristic information is time sequence characteristic information or space sequence characteristic information;

performing characteristic conversion on the currently processed acquisition behavior by referring to the characteristic conversion relation between the first characteristic information and the target acquisition behavior node strategy to obtain the acquisition characteristic information of the currently processed acquisition behavior;

detecting the collected characteristic information to obtain second characteristic information of a target collection behavior node, and determining a target collection behavior from the collected characteristic information to obtain the aggregation information of the target collection behavior;

when the relative acquisition behaviors are multiple, classifying the aggregation information of the target acquisition behaviors determined according to the first characteristic information of the target acquisition behavior node in each relative acquisition behavior to obtain the final aggregation information of the target acquisition behavior;

respectively obtaining final second characteristic information of the target acquisition behavior node according to second characteristic information determined by the first characteristic information of the target acquisition behavior node in each relative acquisition behavior;

and mapping the final target acquisition behavior aggregation information and the final target acquisition behavior node second characteristic information to the currently processed acquisition behavior to obtain the corresponding building information acquisition behavior.

In a possible design of the first aspect, the step of respectively performing feature extraction on the building information collection behaviors corresponding to any two building information collection nodes to obtain corresponding collection behavior features includes:

classifying the building information acquisition behaviors corresponding to the building information acquisition nodes to obtain a plurality of classified acquisition behaviors, wherein the classified acquisition behaviors are acquisition behaviors taking time as an acquisition characteristic, acquisition behaviors taking azimuth as an acquisition characteristic or acquisition behaviors taking acquisition frequency as an acquisition characteristic;

respectively carrying out feature calculation on each classified acquisition behavior in the plurality of classified acquisition behaviors to obtain a classified acquisition feature corresponding to each classified acquisition behavior;

respectively determining the classified acquisition behaviors based on the classified acquisition features corresponding to the classified acquisition behaviors to obtain corrected acquisition features corresponding to the classified acquisition features;

determining a characteristic inverse transformation model corresponding to each corrected acquisition characteristic based on the classified acquisition characteristic corresponding to each classified acquisition behavior;

determining the characteristic point range of each characteristic unit point in the corresponding correction acquisition characteristics in the classified acquisition behaviors according to the characteristic inverse conversion model, and calculating the coincidence rate between any two characteristic point ranges corresponding to different correction acquisition characteristics;

determining whether the coincidence rate between the two feature point ranges corresponding to different correction acquisition features is not less than a set threshold, and determining that the two feature point ranges corresponding to different correction acquisition features correspond to the same acquisition behavior feature when the coincidence rate between the two feature point ranges corresponding to different correction acquisition features is not less than the set threshold;

generating a feature set of the same collection behavior feature by using the feature point range or the feature unit point of the same collection behavior feature;

and acquiring the acquisition behavior characteristics of the building information acquisition behaviors corresponding to the building information acquisition nodes according to the characteristic set of the same acquisition behavior characteristics.

In a possible design of the first aspect, any two of the acquisition behavior features are a first acquisition behavior feature and a second acquisition behavior feature, respectively;

the step of analyzing the relevance between the features of any two extracted collection behavior features to obtain feature relevance information between any two collection behavior features includes:

constructing a first feature association model corresponding to the first collection behavior feature according to a preset collection sampling value corresponding to the first collection behavior feature, wherein a node sampling value between any two adjacent feature association nodes in the first feature association model is the preset collection sampling value, constructing a second feature association model corresponding to the second collection behavior feature according to a preset collection sampling value corresponding to the second collection behavior feature, wherein a node sampling value between any two adjacent feature association nodes in the second feature association model is the preset collection sampling value, the first feature association model and the second feature association model respectively comprise a plurality of feature association nodes in different sampling ranges, the node sampling value is used for representing the size of data sampled in the association process of the feature, and the feature association node is used for aiming at each unit of the first collection behavior feature or the second collection behavior feature Carrying out feature association calculation on feature data of the data range;

extracting initial feature data of the first acquisition behavior feature at any feature association node of the first feature association model, and determining the feature association node with the minimum sampling range in the second feature association model as a target feature association node;

mapping the initial characteristic data to a preset characteristic mapping space to obtain an initial space coordinate point, wherein the preset characteristic mapping space is a three-dimensional space with the characteristic data as a reference, and the three-dimensional space is used for representing a characteristic mapping position of the characteristic data;

converting the initial space coordinate point into a coordinate space of the second acquisition behavior characteristic according to the relative position and the relative orientation information between the building information acquisition node corresponding to the first acquisition behavior characteristic and the building information acquisition node corresponding to the second acquisition behavior characteristic to obtain a converted space coordinate point;

mapping the conversion space coordinate point to a plane with unit association degree under the coordinate space of the second acquisition behavior characteristic to obtain a conversion mapping point, performing single-point coordinate transformation on the conversion mapping point, and projecting the conversion mapping point to the target characteristic association node to obtain an initial mapping point, wherein the unit association degree is a set fixed association degree in the coordinate space of the second acquisition behavior characteristic;

acquiring a related characteristic path in a characteristic related node from the initial characteristic data, wherein the related characteristic path is a nearby characteristic point based on the initial characteristic data, and the related characteristic path is used for representing a related sequence number of the initial characteristic data in the characteristic related node;

mapping the associated characteristic path to the target characteristic associated node, obtaining an associated mapping point in the target characteristic associated node, and generating an association matrix between the first acquisition behavior characteristic and the second acquisition behavior characteristic according to the position relationship between the initial characteristic data and the associated characteristic path, the initial mapping point and the associated mapping point;

acquiring a related data block in the target characteristic related node by taking the initial mapping point as a reference point, mapping the related data block to a characteristic related node where the initial characteristic data is located according to an inverse related matrix corresponding to the related matrix, obtaining a target related data block corresponding to the related data block in the characteristic related node where the initial characteristic data is located, and determining a data area of a related range of the target related data block as a target data area, wherein the related data block is used for representing unit record data with the related range corresponding to the target characteristic related node;

mapping the conversion space coordinate point corresponding to the initial characteristic data to a plane with minimum relevance information in the coordinate space of the second acquisition behavior characteristic to obtain a minimum mapping point, and mapping the conversion space coordinate point corresponding to the initial characteristic data to a plane with maximum relevance information in the coordinate space of the second acquisition behavior characteristic to obtain a maximum mapping point;

determining the mapping deviation of the minimum mapping point and the maximum mapping point as an initial mapping deviation range, performing single-point coordinate transformation on each associated characteristic path in the initial mapping deviation range, and projecting each transformed associated characteristic path into the target characteristic associated node according to the building information acquisition node model to obtain a target associated matching range;

determining a plurality of data points to be associated corresponding to the target data area in the target feature associated node based on the target association matching range, and generating a data block to be associated corresponding to each data point to be associated according to the data points to be associated;

respectively obtaining the association degree between each data block to be associated and the target associated data block, and determining a maximum data point to be associated and a secondary maximum data point to be associated in the multiple data points to be associated according to the association degree;

if the maximum data point to be associated and the next largest data point to be associated meet set conditions, determining the maximum data point to be associated as a maximum point of the target data area in the target feature associated node and taking the maximum data point to be associated as a target maximum point;

if the number of layers of the target feature association node is greater than the number of layers of the target data area in the first feature association model, projecting the target maximum point and the target association matching range to a next feature association node of the target feature association node;

determining a mapping deviation range to be searched by taking the maximum point of the projected target as a reference point in the projected target association matching range based on the set search range;

acquiring a target maximum point on the mapping deviation range to be searched in the next characteristic correlation node, determining the next characteristic correlation node as the target characteristic correlation node, and determining the maximum point in the next characteristic correlation node as the target maximum point;

if the hierarchy of the target feature association node in the second feature association model is consistent with the hierarchy of the target data area in the first feature association model, acquiring a local association data block in the target association data block by taking the target data area as a center, taking the target maximum point determined for the last time as an adjusting point, and acquiring an adjusting association data block by taking the adjusting point as a reference point in the feature association node where the adjusting point is located, wherein the size of the data area of the adjusting association data block is the same as the size of the data area of the local association data block;

adjusting the adjustment associated data block according to the data range in the adjustment associated data block and the data range in the local associated data block;

and obtaining feature association information between any two collection behavior features according to the data information in the adjusted association data block after the position adjustment.

In a possible design of the first aspect, the step of determining a task adjustment policy of the collection task according to feature association information between any two collection behavior features includes:

according to feature association information between any two collection behavior features, task setting information of feature association information between every two collection behavior features in each unit project in the collection task is obtained one by one, and the unit project is any one of a plurality of task projects in the collection task;

and when the task setting information of each unit item of every two acquired behavior characteristics is obtained, adjusting the unit items according to the association level calculated by the characteristic association information and the adjustment range corresponding to the association level to obtain the adjusted unit items of every two acquired behavior characteristics, adjusting the task parameters of every two adjusted unit items of the acquired behavior characteristics according to the acquisition parameters used when the acquired behavior characteristics are acquired, and so on to determine the task adjustment strategy of the acquisition task, wherein the acquisition parameters are at least one of the time sequence parameters, the empty sequence parameters or the frequency sequence parameters of the building information acquisition nodes corresponding to the acquired behavior characteristics when the acquired behavior characteristics are acquired.

In a second aspect, an embodiment of the present invention further provides an intelligent building information monitoring method, which is applied to a server, where the server is in communication connection with a building information collection node, and the method includes:

respectively acquiring corresponding building information acquisition behaviors from at least two building information acquisition nodes which are at the same acquisition position and are associated with a set acquisition task, wherein the associated acquisition task refers to the fact that logical association exists in the acquisition process of a target information acquisition object;

respectively extracting the characteristics of building information acquisition behaviors corresponding to any two building information acquisition nodes to obtain corresponding acquisition behavior characteristics;

performing characteristic correlation analysis on any two extracted collection behavior characteristics to obtain characteristic correlation information between any two collection behavior characteristics;

and determining a task adjustment strategy of the acquisition task according to the characteristic association information between any two acquisition behavior characteristics, and respectively sending the task adjustment strategy to the corresponding at least two building information acquisition nodes, so that the building information acquisition nodes adjust the acquisition task according to the task adjustment strategy.

In a third aspect, an embodiment of the present invention further provides an intelligent building information monitoring apparatus, which is applied to a server, where the server is in communication connection with a building information collection node, and the apparatus includes:

the acquisition module is used for respectively acquiring corresponding building information acquisition behaviors from at least two building information acquisition nodes which are at the same acquisition position and are associated with a set acquisition task, wherein the associated acquisition task refers to the fact that logical association exists in the acquisition process of a target information acquisition object;

the characteristic extraction module is used for respectively extracting the characteristics of building information acquisition behaviors corresponding to any two building information acquisition nodes to obtain corresponding acquisition behavior characteristics;

the analysis module is used for carrying out feature correlation analysis on any two extracted collection behavior features to obtain feature correlation information between any two collection behavior features;

and the determining and sending module is used for determining a task adjustment strategy of the acquisition task according to the characteristic association information between any two acquisition behavior characteristics, and sending the task adjustment strategy to the at least two corresponding building information acquisition nodes respectively, so that the building information acquisition nodes adjust the acquisition task according to the task adjustment strategy.

In a fourth aspect, an embodiment of the present invention further provides an intelligent building system, where the intelligent building system includes a server and a building information acquisition node in communication connection with the server;

the server is used for respectively acquiring corresponding building information acquisition behaviors from at least two building information acquisition nodes which are at the same acquisition position and are associated with a set acquisition task, wherein the associated acquisition task refers to the fact that logical association exists in the acquisition process of a target information acquisition object;

the server is used for respectively extracting the characteristics of building information acquisition behaviors corresponding to any two building information acquisition nodes to obtain corresponding acquisition behavior characteristics;

the server is used for carrying out feature correlation analysis on any two extracted collected behavior features to obtain feature correlation information between any two collected behavior features;

the server is used for determining task adjustment strategies of the acquisition tasks according to feature association information between any two acquisition behavior features and respectively sending the task adjustment strategies to the at least two corresponding building information acquisition nodes;

and the building information acquisition node is used for adjusting the acquisition task according to the task adjustment strategy.

In a fifth aspect, an embodiment of the present invention further provides a server, where the server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one building information collection node, the machine-readable storage medium is configured to store a program, instructions, or codes, and the processor is configured to execute the program, instructions, or codes in the machine-readable storage medium to perform the intelligent building information monitoring method in the second aspect.

In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores instructions that, when implemented on a computer, cause the computer to execute the intelligent building information monitoring method in the second aspect.

Based on any one aspect, the invention obtains the collection behavior characteristics of the corresponding building information collection behaviors from at least two building information collection nodes which are at the same collection position and are associated with the set collection task respectively, and then performs correlation analysis between the characteristics on any two collection behavior characteristics to obtain the characteristic correlation information between any two collection behavior characteristics, thereby determining the monitoring adjustment strategy of the collection task according to the characteristic correlation information between any two collection behavior characteristics. Therefore, the method and the system aim at monitoring the acquisition behaviors of the linkage acquisition process of the building information acquisition nodes, so that whether the issued acquisition tasks can achieve the expected and reasonable effect in the actual linkage acquisition process or not can be conveniently determined subsequently, and the method and the system can aim at the acquisition tasks through characteristic correlation information among the characteristics of the acquisition behaviors and improve the reliability of the linkage acquisition process.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic view of an application scenario of an intelligent building system according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of an intelligent building information monitoring method according to an embodiment of the present invention;

fig. 3 is a second schematic flowchart of the intelligent building information monitoring method according to the embodiment of the present invention;

fig. 4 is a schematic functional module diagram of an intelligent building information monitoring apparatus according to an embodiment of the present invention;

fig. 5 is a schematic block diagram of a server for implementing the intelligent building information monitoring method according to an embodiment of the present invention.

Detailed Description

The present invention is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the apparatus embodiments or the system embodiments. In the description of the present invention, "at least one" includes one or more unless otherwise specified. "plurality" means two or more. For example, at least one of A, B and C, comprising: a alone, B alone, a and B in combination, a and C in combination, B and C in combination, and A, B and C in combination. In the present invention, "/" indicates "or" means, for example, A/B may indicate A or B; "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone.

Fig. 1 is an interactive schematic diagram of an intelligent building system 10 provided by an embodiment of the invention. The intelligent building system 10 may include a server 100 and a building information collection node 200 communicatively connected to the server 100, and the server 100 may include a processor for executing instruction operations. The intelligent building system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the intelligent building system 10 may include only one of the components shown in fig. 1 or may also include other components.

In some embodiments, the server 100 may be a single server or a group of servers. The set of operating servers may be centralized or distributed (e.g., the server 100 may be a distributed system). In some embodiments, the server 100 may be local or remote to the building information collection node 200. For example, the server 100 may access information stored in the building information collection node 200 and a database, or any combination thereof, via a network. As another example, the server 100 may be directly connected to at least one of the building information collection node 200 and a database to access information and/or data stored therein. In some embodiments, the server 100 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof.

In some embodiments, the server 100 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described in this disclosure. A processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set computer (Reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.

The network may be used for the exchange of information and/or data. In some embodiments, one or more components (e.g., the server 100, the building information collection node 200, and the database) in the intelligent building system 10 may send information and/or data to other components. In some embodiments, the network may be any type of wired or wireless network, or combination thereof. Merely by way of example, Network 130 may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a WLAN, a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the intelligent building system 10 may connect to the network to exchange data and/or information.

The aforementioned database may store data and/or instructions. In some embodiments, the database may store data obtained from the building information collection node 200. In some embodiments, the database may store data and/or instructions for the exemplary methods described in this disclosure. In some embodiments, the database may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, the database may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.

In some embodiments, a database may be connected to the network to communicate with one or more components in the intelligent building system 10 (e.g., the server 100, the building information collection node 200, etc.). One or more components in the intelligent building system 10 may access data or instructions stored in a database via a network. In some embodiments, the database may be directly connected to one or more components in the intelligent building system 10 (e.g., the server 100, the building information collection node 200, etc.); alternatively, in some embodiments, the database may also be part of the server 100.

The building information collection node 200 may be configured to collect building information, such as information of various sensors, information of various image sensors, information of various verification terminals (a face scanning terminal, a parking charge scanning terminal, and the like), and is not limited herein.

To solve the technical problem in the background art, fig. 2 is a schematic flowchart of an intelligent building information monitoring method according to an embodiment of the present invention, which can be executed by the intelligent building system 10 shown in fig. 1, and the intelligent building information monitoring method is described in detail below.

In step S110, the server 100 obtains corresponding building information collection behaviors from at least two building information collection nodes 200 at the same collection position and associated with a set collection task, where the associated collection task is a collection process for a target information collection object and has a logical association.

For example, when a collection task is issued for a certain landmark monitored object in a building, two building information collection nodes 200 for collecting the landmark monitored object are sequentially matched to collect building collection information of the landmark monitored object at different time intervals, and if a certain building information collection node 200 collects building collection information of the landmark monitored object in a certain block, another building information collection node 200 immediately collects building collection information of the landmark monitored object in a certain block, this indicates that the two building information collection nodes 200 have a logical association in the collection process of the target information collection object.

It is understood that in other possible designs, a person skilled in the art may determine what the associated acquisition tasks are according to actual requirements, only to ensure that the acquisition tasks are not acquired independently.

In step S120, the server 100 extracts features of building information collection behaviors corresponding to any two building information collection nodes 200, respectively, to obtain corresponding collection behavior features.

In step S130, the server 100 performs feature correlation analysis on any two extracted collected behavior features to obtain feature correlation information between any two collected behavior features.

In step S140, the server 100 determines task adjustment strategies of the collection tasks according to the feature association information between any two collection behavior features, and sends the task adjustment strategies to the corresponding at least two building information collection nodes 200 respectively.

In step S150, the building information collection node 200 adjusts the collection task according to the task adjustment policy.

Based on the above steps, in this embodiment, the collection behavior features of the corresponding building information collection behaviors are respectively obtained from at least two building information collection nodes 200 which are at the same collection position and are associated with the set collection task, and then, the association analysis between the features is performed on any two collection behavior features to obtain the feature association information between any two collection behavior features, so that the monitoring adjustment strategy of the collection task is determined according to the feature association information between any two collection behavior features. Therefore, the invention aims at monitoring the collection behaviors of the linkage collection process of the building information collection node 200, thereby being convenient for determining whether the issued collection task can achieve the expected and reasonable effect in the actual linkage collection process or not, and aiming at the collection task by adjusting the characteristic correlation information among the collection behavior characteristics in a targeted manner, and improving the reliability of the linkage collection process.

In one possible design, for step S110, the present embodiment may respectively acquire integration characteristic information of corresponding acquisition process record information from at least two building information acquisition nodes 200 associated with the same acquisition location and the set acquisition task, where the integration characteristic information is used to represent acquisition process variation conditions of the acquisition process record information within an integration time period, for example, the acquisition process variation conditions include one or more combinations of acquisition orientation variation conditions, acquisition frequency variation conditions, and acquisition time period variation conditions.

Then, the integrated feature information may be input into a second feature expression matrix of the behavior transformation matrix that is updated last time, so as to obtain a plurality of first behavior prediction units and a second prediction behavior corresponding to each first behavior prediction unit. The behavior transformation matrix comprises a first characteristic expression matrix and a second characteristic expression matrix, the first characteristic expression matrix is used for extracting integrated characteristic information of the collected process record information, the second characteristic expression matrix is used for generating a plurality of first behavior prediction units of the collected process record information according to the integrated characteristic information, and second prediction behaviors are generated for each first behavior prediction unit.

Then, the first predicted behaviors corresponding to the behavior prediction units may be selected from the first behavior prediction units according to the second predicted behavior. The first prediction behavior corresponding to each behavior prediction unit is obtained by analyzing the inside of the behavior prediction unit based on the behavior conversion matrix updated last time, the acquisition process record information comprises at least one acquisition process node, one acquisition process node corresponds to at least one behavior prediction unit, and the acquisition process record information is added with the behavior information of the behavior unit corresponding to each acquisition process node.

Then, at least one target behavior prediction unit can be selected from the multiple behavior prediction units according to the first predicted behavior to form a target behavior prediction unit group, and behavior extraction information corresponding to the behavior transformation matrix is determined according to the coverage range value between each target behavior prediction unit in the target behavior prediction unit group and the behavior unit of each acquisition process node in the acquisition process record information. The behavior extraction information may include behavior information of the target behavior prediction unit corresponding to the coverage range value greater than the set range value.

Then, the initial characteristic information of the target collection behavior node can be detected from the initial collection behavior of the behavior extraction information according to the behavior extraction information corresponding to the behavior conversion matrix.

Then, the initial collection behavior may be used as a previous collection behavior and the initial feature information may be used as previous feature information, and feature conversion may be performed on a subsequent collection behavior of the previous collection behavior according to a feature conversion relationship between the previous feature information and a target collection behavior node policy to obtain collection feature information in the subsequent collection behavior, where the target collection behavior node policy is used to represent preset feature information of a preset target collection behavior node.

Then, the collected characteristic information in the next collection behavior can be detected to obtain the first characteristic information of the target collection behavior node in the next collection behavior.

And then, taking the next acquisition behavior as the previous acquisition behavior and the first characteristic information of the target acquisition behavior node in the next acquisition behavior as the previous characteristic information, returning to the step of performing characteristic conversion on the next acquisition behavior of the previous acquisition behavior according to the characteristic conversion relation between the previous characteristic information and the target acquisition behavior node strategy to obtain the acquisition characteristic information in the next acquisition behavior, and performing iterative processing until the first characteristic information of the target acquisition behavior node in the last acquisition behavior of the behavior extraction information is obtained.

Then, the last collection behavior may be regarded as the previous collection behavior of the initial collection behavior, and the first feature information of the final target collection behavior node in the initial collection behavior may be determined with reference to the first feature information of the target collection behavior node in the last collection behavior.

Then, the currently processed acquisition behaviors may be sequentially selected from the behavior extraction information according to a set characteristic sequence, where the set characteristic sequence is a time sequence, a null sequence, or a frequency sequence, the time sequence is used to represent a sequence based on time, the null sequence is used to represent a sequence based on a spatial orientation, and the frequency sequence is used to represent a sequence based on frequency.

Then, a relative acquisition behavior may be determined from acquisition behaviors in which the set feature order in the behavior extraction information is located before the acquisition behavior of the current process, wherein the relative acquisition behavior is the acquisition behavior associated with the set feature order corresponding to the acquisition behavior of the current process.

Then, first characteristic information of a target acquisition behavior node in the relative acquisition behavior can be acquired, wherein the first characteristic information is time sequence characteristic information or space sequence characteristic information.

Then, feature conversion can be performed on the currently processed acquisition behavior by referring to a feature conversion relationship between the first feature information and the target acquisition behavior node policy, so as to obtain the acquisition feature information of the currently processed acquisition behavior.

Then, the collected characteristic information can be detected to obtain second characteristic information of the target collection behavior node, and the target collection behavior is determined from the collected characteristic information to obtain the aggregation information of the target collection behavior.

And when the relative acquisition behaviors are multiple, classifying the aggregation information of the target acquisition behaviors determined according to the first characteristic information of the target acquisition behavior node in each relative acquisition behavior to obtain the final aggregation information of the target acquisition behavior.

And then, obtaining the final second characteristic information of the target acquisition behavior node according to the second characteristic information determined by the first characteristic information of the target acquisition behavior node in each relative acquisition behavior.

Then, the final aggregate information of the target acquisition behaviors and the final second characteristic information of the target acquisition behavior nodes can be mapped to the currently processed acquisition behaviors, so that the corresponding building information acquisition behaviors are obtained.

In a possible design, for step S120, the present embodiment may classify the building information collection behaviors corresponding to the building information collection nodes 200 to obtain a plurality of classified collection behaviors, where the classified collection behaviors are collection behaviors with time as a collection characteristic, collection behaviors with orientation as a collection characteristic, or collection behaviors with collection frequency as a collection characteristic.

Then, feature calculation can be performed on each classified collection behavior in the plurality of classified collection behaviors to obtain a classified collection feature corresponding to each classified collection behavior.

Then, the classified collection behavior can be determined based on the classified collection characteristic corresponding to each classified collection behavior, so as to obtain a corrected collection characteristic corresponding to each classified collection characteristic.

Then, a feature inverse transformation model corresponding to each modified acquisition feature may be determined based on the classified acquisition feature corresponding to each classified acquisition behavior.

Then, the feature point range of each feature unit point in the corresponding correction acquisition features in the classification acquisition behavior can be determined according to the feature inverse transformation model, and the coincidence rate between any two feature point ranges corresponding to different correction acquisition features is calculated.

Then, whether the coincidence rate between the feature point ranges corresponding to the different correction acquisition features is not smaller than a set threshold value or not can be determined, and when the coincidence rate between the feature point ranges corresponding to the different correction acquisition features is not smaller than the set threshold value, the feature point ranges corresponding to the different correction acquisition features are determined to correspond to the same acquisition behavior feature.

Then, the feature point range or the feature unit point of the same acquisition behavior feature can be used for generating a feature set of the same acquisition behavior feature.

Then, the collection behavior characteristics of the building information collection behavior corresponding to the building information collection node 200 can be obtained according to each same collection behavior characteristic feature set.

In one possible design, any two of the aforementioned acquisition behavior features are a first acquisition behavior feature and a second acquisition behavior feature, respectively.

For step S130, in this embodiment, a first feature association model corresponding to the first collection behavior feature may be constructed according to a preset collection sampling value corresponding to the first collection behavior feature, a node sampling value between any two adjacent feature association nodes in the first feature association model is a preset collection sampling value, a second feature association model corresponding to the second collection behavior feature is constructed according to a preset collection sampling value corresponding to the second collection behavior feature, a node sampling value between any two adjacent feature association nodes in the second feature association model is a preset collection sampling value, the first feature association model and the second feature association model respectively include a plurality of feature association nodes in different sampling ranges, where the node sampling value is used to represent a data amount of a feature sampled in an association process, and the feature association node is used to perform feature association on feature data of each unit data range in the first collection behavior feature or the second collection behavior feature And (4) calculating.

Then, initial feature data of the first acquisition behavior feature at any feature association node of the first feature association model can be extracted, and the feature association node with the minimum sampling range in the second feature association model is determined as a target feature association node.

Then, the initial feature data may be mapped to a preset feature mapping space, so as to obtain an initial spatial coordinate point, where the preset feature mapping space is a three-dimensional space based on the feature data, and the three-dimensional space is used for representing a feature mapping position of the feature data.

Then, the initial spatial coordinate point may be converted into a coordinate space of the second collection behavior characteristic according to the relative position and the relative orientation information between the building information collection node 200 corresponding to the first collection behavior characteristic and the building information collection node 200 corresponding to the second collection behavior characteristic, so as to obtain a converted spatial coordinate point.

Then, the transformation space coordinate point may be mapped onto a plane having a unit association degree in the coordinate space of the second collection behavior feature to obtain a transformation mapping point, the transformation mapping point is subjected to single-point coordinate transformation, and the transformation mapping point is projected into a target feature association node to obtain an initial mapping point, where the unit association degree is a set fixed association degree in the coordinate space of the second collection behavior feature.

Next, a relevant feature path may be obtained in the feature-related node from the initial feature data, where the relevant feature path is a nearby feature point with reference to the initial feature data, and the relevant feature path is used to indicate a serial number of a relevant sequence of the initial feature data in the feature-related node.

Then, the associated feature path may be mapped to a target feature associated node, an associated mapping point is obtained in the target feature associated node, and an association matrix between the first collection behavior feature and the second collection behavior feature is generated according to the position relationship between the initial feature data and the associated feature path, the initial mapping point, and the associated mapping point.

Then, a related data block may be acquired in the target feature related node with the initial mapping point as a reference point, the related data block may be mapped to the feature related node where the initial feature data is located according to an inverse correlation matrix corresponding to the correlation matrix, a target related data block corresponding to the related data block may be acquired in the feature related node where the initial feature data is located, and a data area of a related range of the target related data block may be determined as a target data area, where the related data block is used to represent unit record data having the related range corresponding to the target feature related node.

Then, the transformed spatial coordinate point corresponding to the initial characteristic data may be mapped onto a plane having minimum relevance information in the coordinate space of the second collection behavior characteristic to obtain a minimum mapping point, and the transformed spatial coordinate point corresponding to the initial characteristic data may be mapped onto a plane having maximum relevance information in the coordinate space of the second collection behavior characteristic to obtain a maximum mapping point.

Then, the mapping deviation between the minimum mapping point and the maximum mapping point may be determined as an initial mapping deviation range, single-point coordinate transformation is performed on each associated feature path in the initial mapping deviation range, and each transformed associated feature path is projected into a target feature associated node according to the building information collection node 200 model, so as to obtain a target associated matching range.

Then, a plurality of data points to be associated corresponding to the target data area in the target feature association node may be determined based on the target association matching range, and a data block to be associated corresponding to each data point to be associated is generated according to the plurality of data points to be associated.

Then, the association degree between each data block to be associated and the target associated data block can be respectively obtained, and the maximum data point to be associated and the secondary maximum data point to be associated in the multiple data points to be associated are determined according to the association degree.

And if the maximum data point to be associated and the next maximum data point to be associated meet the set conditions, determining the maximum data point to be associated as the maximum point of the target data area in the target characteristic associated node and taking the maximum data point as the target maximum point.

And if the number of layers of the target feature associated node is greater than that of the target data area in the first feature associated model, projecting the target maximum point and the target associated matching range to a next feature associated node of the target feature associated node.

Then, based on the set search range, a mapping deviation range to be searched, which takes the maximum point of the projected target as a reference point, may be determined in the projected target associated matching range.

Then, a target maximum point on the to-be-searched mapping deviation range in the next feature related node may be obtained, and the next feature related node is determined as a target feature related node, and the maximum point in the next feature related node is determined as a target maximum point.

If the hierarchy of the target feature associated node in the second feature associated model is consistent with the hierarchy of the target data area in the first feature associated model, a local associated data block is obtained in the target associated data block by taking the target data area as the center, the target maximum point determined at the last time is taken as an adjusting point, an adjusting associated data block with the adjusting point as a reference point is obtained in the feature associated node where the adjusting point is located, and the size of the data area of the adjusting associated data block is the same as that of the data area of the local associated data block.

The adjustment associated data block may then be adjusted based on the data range in the adjustment associated data block and the data range in the local associated data block.

Then, feature association information between any two collection behavior features can be obtained according to the data information in the adjusted association data block after the position adjustment.

In a possible design, for step S140, the present embodiment may obtain task setting information of each unit item of the feature association information between every two collection behavior features in the collection task one by one according to the feature association information between all any two collection behavior features, where a unit item is any one unit of a plurality of task items in the collection task.

Then, every time the task setting information of each unit item of every two acquired behavior characteristics is obtained, the unit items are adjusted according to the adjustment range corresponding to the association level according to the association level obtained by calculating the characteristic association information, the adjusted unit items of every two acquired behavior characteristics are obtained, the task parameter adjustment is performed on the adjusted unit items of every two acquired behavior characteristics according to the acquisition parameters used when every two acquired behavior characteristics are acquired, and the like, so as to determine the task adjustment strategy of the acquisition task, wherein the acquisition parameters are at least one of the time sequence parameters, the space sequence parameters or the frequency sequence parameters of the building information acquisition node 200 corresponding to the acquired behavior characteristics when the acquired behavior characteristics are acquired.

Fig. 4 shows a flow chart of another intelligent building information monitoring method according to the present invention, which is different from the above embodiment, the intelligent building information monitoring method is executed by the server 100, it is understood that the steps involved in the intelligent building information monitoring method to be described next have been described in the above embodiment, and the detailed contents of the specific steps can be described with reference to the above embodiment, and only the steps executed by the server 100 will be briefly described below.

Step S210, respectively acquiring corresponding building information collection behaviors from at least two building information collection nodes 200 at the same collection position and associated with a set collection task, where the associated collection task is a collection process for a target information collection object and has a logical association.

Step S220, feature extraction is performed on building information acquisition behaviors corresponding to any two building information acquisition nodes 200, respectively, to obtain corresponding acquisition behavior features.

Step S230, performing feature correlation analysis on any two extracted collection behavior features to obtain feature correlation information between any two collection behavior features.

Step S240, determining a task adjustment policy of the collection task according to the feature association information between any two collection behavior features, and sending the task adjustment policy to the corresponding at least two building information collection nodes 200, so that the building information collection nodes 200 adjust the collection task according to the task adjustment policy.

Fig. 4 is a schematic diagram of functional modules of an intelligent building information monitoring apparatus 300 according to an embodiment of the present invention, and this embodiment may divide the functional modules of the intelligent building information monitoring apparatus 300 according to the method embodiment executed by the server 100. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that the division of the modules in the present invention is illustrative, and is only a logical function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the intelligent building information monitoring apparatus 300 shown in fig. 4 is only a schematic diagram of the apparatus. The intelligent building information monitoring apparatus 300 may include an obtaining module 310, a feature extracting module 320, an analyzing module 330, and a determining and sending module 340, and the functions of the functional modules of the intelligent building information monitoring apparatus 300 are described in detail below.

The acquiring module 310 is configured to acquire corresponding building information acquisition behaviors from at least two building information acquisition nodes 200 at the same acquisition position and associated with a set acquisition task, where the associated acquisition task is a logical association in an acquisition process for a target information acquisition object.

The feature extraction module 320 is configured to perform feature extraction on building information acquisition behaviors corresponding to any two building information acquisition nodes 200, respectively, to obtain corresponding acquisition behavior features.

The analysis module 330 is configured to perform feature correlation analysis on any two extracted collection behavior features to obtain feature correlation information between any two collection behavior features.

The determining and sending module 340 is configured to determine a task adjustment policy of the collection task according to the feature association information between any two collection behavior features, and send the task adjustment policy to the corresponding at least two building information collection nodes 200, so that the building information collection nodes 200 adjust the collection task according to the task adjustment policy.

Further, fig. 5 is a schematic structural diagram of a server 100 for performing the intelligent building information monitoring method according to an embodiment of the present invention. As shown in FIG. 5, the server 100 may include a network interface 110, a machine-readable storage medium 120, a processor 130, and a bus 140. The processor 130 may be one or more, and one processor 130 is illustrated in fig. 5 as an example. The network interface 110, the machine-readable storage medium 120, and the processor 130 may be connected by a bus 140 or otherwise, as exemplified by the connection by the bus 140 in fig. 5.

The machine-readable storage medium 120 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the intelligent building information monitoring method in the embodiment of the present invention (for example, the obtaining module 310, the feature extracting module 320, the analyzing module 330, and the determining and sending module 340 of the intelligent building information monitoring apparatus 300 shown in fig. 4). The processor 130 executes various functional applications and data processing of the terminal device by detecting the software programs, instructions and modules stored in the machine-readable storage medium 120, that is, the above-mentioned intelligent building information monitoring method is implemented, and details are not repeated herein.

The machine-readable storage medium 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the machine-readable storage medium 120 may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memories of the systems and methods described herein are intended to comprise, without being limited to, these and any other suitable memory of a publishing node. In some examples, the machine-readable storage medium 120 may further include memory located remotely from the processor 130, which may be connected to the server 100 over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor 130 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 130. The processor 130 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.

The server 100 may interact with other devices (e.g., the building information collection node 200) via the network interface 110. Network interface 110 may be a circuit, bus, transceiver, or any other device that may be used to exchange information. Processor 130 may send and receive information using network interface 110.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the embodiments of the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.

Claims

1. An intelligent building information monitoring method is applied to an intelligent building system, the intelligent building system comprises a server and building information acquisition nodes in communication connection with the server, and the method comprises the following steps:

the building information acquisition node adjusts the acquisition task according to the task adjustment strategy;

the step of determining the task adjustment strategy of the collection task according to the feature association information between any two collection behavior features includes:

2. The intelligent building information monitoring method according to claim 1, wherein the step of acquiring the corresponding building information collection behaviors from at least two building information collection nodes associated with the same collection position and the set collection task, respectively, comprises:

3. The intelligent building information monitoring method according to claim 1, wherein the step of performing feature extraction on building information collection behaviors corresponding to any two building information collection nodes respectively to obtain corresponding collection behavior features comprises:

4. The intelligent building information monitoring method according to claim 1, wherein any two of the collection behavior features are a first collection behavior feature and a second collection behavior feature, respectively;

5. An intelligent building information monitoring method is applied to a server, the server is in communication connection with building information acquisition nodes, and the method comprises the following steps:

determining a task adjustment strategy of the collection task according to feature association information between any two collection behavior features, and respectively sending the task adjustment strategy to the corresponding at least two building information collection nodes, so that the building information collection nodes adjust the collection task according to the task adjustment strategy, and determining the task adjustment strategy of the collection task according to the feature association information between any two collection behavior features comprises the following steps:

6. The utility model provides an intelligent building information monitoring device which characterized in that is applied to the server, server and building information acquisition node communication connection, the device includes:

the determining and sending module is used for determining a task adjustment strategy of the acquisition task according to feature association information between any two acquisition behavior features, and sending the task adjustment strategy to the at least two corresponding building information acquisition nodes respectively so that the building information acquisition nodes adjust the acquisition task according to the task adjustment strategy, and the determining and sending module determines the task adjustment strategy of the acquisition task according to the feature association information between any two acquisition behavior features in the following way:

7. The intelligent building system is characterized by comprising a server and building information acquisition nodes in communication connection with the server;

the building information acquisition node is used for adjusting the acquisition task according to the task adjustment strategy, and the server determines the task adjustment strategy of the acquisition task according to the characteristic association information between any two acquisition behavior characteristics in the following way:

8. A server, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected by a bus system, the network interface is configured to be communicatively connected to at least one building information collection node, the machine-readable storage medium is configured to store a program, instructions, or code, and the processor is configured to execute the program, instructions, or code in the machine-readable storage medium to perform the intelligent building information monitoring method of claim 5.

9. A readable storage medium having stored therein instructions that, when executed, perform the intelligent building information monitoring method of claim 5.