CN111695884B - Internet of things big data visualization method and system based on smart construction site - Google Patents
Internet of things big data visualization method and system based on smart construction site Download PDFInfo
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- CN111695884B CN111695884B CN202010823197.4A CN202010823197A CN111695884B CN 111695884 B CN111695884 B CN 111695884B CN 202010823197 A CN202010823197 A CN 202010823197A CN 111695884 B CN111695884 B CN 111695884B
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Abstract
According to the method and system for visualizing the Internet of things big data based on the intelligent construction site, the Internet of things equipment uploads a packaged running data packet to a big data server, the big data server determines configuration parameters of a data conversion thread corresponding to equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet to start the corresponding data conversion thread so as to transcode the equipment running data to obtain target transcoding data, then a construction track state diagram is obtained according to the target transcoding data and displayed visually, and finally a track parameter set in the current time step generates a control instruction and issues the control instruction when the abnormality of the construction state track diagram is monitored. According to the invention, the equipment operation data is transcoded and then analyzed, so that the monitoring delay can be reduced and the visualization of the monitoring data can be realized.
Description
Technical Field
The disclosure relates to the technical field of Internet of things and big data processing, in particular to an Internet of things big data visualization method and system based on an intelligent construction site.
Background
With the development of science and technology, the technology of the internet of things is applied to various fields, and the production operation efficiency is effectively improved. The intelligent construction site is characterized in that an informatization means is used for establishing a construction project informatization ecological circle with interconnection cooperation, intelligent production and scientific management, and then project operation data in the ecological circle are mined and analyzed, so that the informatization level of engineering management is improved, and the intelligent management of engineering construction is realized. The key of guaranteeing the normal and reliable operation of wisdom building site is used wisdom building site and realizes the production control to the wisdom building site with internet of things. However, in the prior art, the technical problems that monitoring delay is long and data visualization cannot be realized often exist when production monitoring is performed on an intelligent construction site.
Disclosure of Invention
In view of the above, the present disclosure aims to provide a method and a system for visualizing big data of an internet of things based on an intelligent construction site.
In a first aspect, a big data visualization method of the internet of things based on an intelligent construction site is provided, and is applied to a big data server and a plurality of pieces of equipment of the internet of things deployed in a target construction site, and the method includes:
each piece of Internet of things equipment periodically packages corresponding equipment operation data to obtain an operation data packet, and uploads the operation data packet to the big data server through a data transmission channel pre-established with the big data server;
the big data server receives a target data packet uploaded by each Internet of things device; determining configuration parameters of a data conversion thread corresponding to the equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet, and starting the corresponding data conversion thread based on the configuration parameters to transcode equipment operation data in the target data packet to obtain target transcoding data;
the big data server determines a construction state track graph of the target construction site according to the obtained multiple groups of target transcoding data and displays the construction state track graph in real time;
performing iterative monitoring on the construction state track graph according to a set time step length to determine whether the construction state track graph is abnormal or not; and when monitoring that the construction state track graph is abnormal, generating a control instruction according to the track parameter set of the construction state track graph in the current time step length and sending the control instruction to each piece of Internet of things equipment.
Optionally, the determining, by the big data server, the construction state trajectory diagram of the target construction site according to the obtained multiple sets of target transcoding data includes:
acquiring calibration transcoding data updated relative to preset transcoding data in a target time period in the multiple groups of target transcoding data;
taking the updated calibration transcoding data as state diagram data to be fitted;
determining state trajectory data to be fitted according to mapping paths of the state diagram data in the multiple groups of target transcoding data and mapping paths of other calibration transcoding data except the state diagram data in the preset transcoding data;
extracting state node data from the data queues of the state diagram data and the state trajectory data;
and fitting the extracted state node data according to the time sequence order to obtain the construction state track diagram of the target construction site.
Optionally, starting a corresponding data conversion thread based on the configuration parameter to transcode the device operation data in the target data packet to obtain target transcoded data, and further comprising:
determining an item tag corresponding to configuration pointing item information of the configuration parameters and thread matching parameters of the configuration pointing item information, wherein the thread matching parameters represent matching paths of the configuration pointing item information of the configuration parameters; the thread matching parameters at least comprise: a first matching path and a second matching path representing configuration pointing project information of the configuration parameter;
extracting a label distribution graph corresponding to the project label, wherein the label distribution graph comprises pre-configured distribution nodes, and the distribution nodes represent path nodes which are positioned on a track curve in the label distribution graph and are configured to point to project information corresponding to the project label; the distribution node includes at least: a first matching path node and a second matching path node representing configuration pointing item information corresponding to distribution information included in the tag distribution map on a trajectory curve in the tag distribution map;
according to the item tag and the thread matching parameter, searching a thread tag matched with the configuration parameter in the tag distribution map, and starting a corresponding data conversion thread based on the thread tag;
and transcoding the equipment operation data through a thread logic list corresponding to the data conversion thread to obtain the target transcoding data.
Optionally, determining, from a preset database, a configuration parameter of a data conversion thread corresponding to the device identifier according to the device identifier of the internet of things device included in each target data packet, specifically including:
performing packet classification on each received target data packet to determine a plurality of data packet classes of the target data packet, and performing class feature screening based on the plurality of data packet classes; the classes of the data packets are the classes corresponding to the sub data packets corresponding to the internet of things equipment in the target data packet;
performing cosine distance calculation of a category field on the target data packet category of the target data packet obtained by screening and each preset data packet category in a preset category set; the preset category set stores category configuration data corresponding to a plurality of preset data packet categories and service labels belonging to the corresponding service behavior information, wherein the preset data packet categories are verified categories of the Internet of things equipment; if the types of the target data packets of the screened target data packets are multiple, calculating the cosine distance of the type field in the following mode: performing traversal interval calculation according to the priority sequence preset for the target data packet type; in each round of interval calculation, the cosine distance of the category field is calculated only based on one of the preset data packet categories, and the preset data packet categories falling into the preset cosine distance interval are added into a list of the next round of interval calculation so as to calculate the cosine distance of the category field based on the next preset data packet category;
determining an equipment identifier to which the target data packet belongs according to a service label of the Internet of things equipment corresponding to a preset data packet type with a cosine distance of a type field of the target data packet meeting a set condition, determining a plurality of initial configuration parameters from a preset database according to the equipment identifier, and determining configuration parameters of a data conversion thread corresponding to the equipment identifier from the plurality of initial configuration parameters based on an encapsulation protocol of the target data packet.
Optionally, iteratively monitoring the construction state trajectory diagram according to a set time step to determine whether the construction state trajectory diagram is abnormal, including:
determining a state change vector of the construction state track graph and each construction service data according to the set time step;
if the construction state track graph contains the self-adaptive correction label based on the state change vector, calculating business connection behavior values between each construction business data of the construction state track graph under the cured data label and each construction business data of the construction state track graph under the self-adaptive correction label according to the construction business data of the construction state track graph under the self-adaptive correction label and the effective construction duration of the construction business data;
capturing construction business data of the construction state trajectory graph under the solidified data label and associated with the construction business data under the adaptive correction label based on the business contact behavior value; if the solidified data label of the construction state track graph contains a plurality of construction service data, calculating a service connection behavior value between each piece of construction service data under the solidified data label of the construction state track graph based on the construction service data under the adaptive correction label of the construction state track graph and the effective construction duration of the construction service data, and then filtering each piece of construction service data under the solidified data label according to the service connection behavior value between each piece of construction service data; capturing the target construction service data remained after the filtering to the adaptive correction label according to the construction service data of the construction state trajectory diagram under the adaptive correction label and the effective construction duration of the construction service data;
periodically calculating a data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; when the matrix characteristic value of the data difference matrix does not exceed a set value, judging that the construction state track graph is not abnormal, and returning to the step of periodically calculating the data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; and judging that the construction state track graph is abnormal when the matrix characteristic value of the data difference matrix exceeds the set value.
The second aspect is used for providing an Internet of things big data visualization system based on an intelligent construction site, and the Internet of things big data visualization system comprises a big data server and a plurality of Internet of things devices, wherein the big data server is communicated with the plurality of Internet of things devices, and the plurality of Internet of things devices are deployed in a target construction site;
the Internet of things equipment is used for:
periodically packaging corresponding equipment operation data to obtain an operation data packet, and uploading the operation data packet to the big data server through a data transmission channel pre-established with the big data server;
the big data server is used for:
receiving a target data packet uploaded by each Internet of things device; determining configuration parameters of a data conversion thread corresponding to the equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet, and starting the corresponding data conversion thread based on the configuration parameters to transcode equipment operation data in the target data packet to obtain target transcoding data;
determining a construction state track graph of the target construction site according to the obtained multiple groups of target transcoding data and displaying the construction state track graph in real time;
performing iterative monitoring on the construction state track graph according to a set time step length to determine whether the construction state track graph is abnormal or not; and when monitoring that the construction state track graph is abnormal, generating a control instruction according to the track parameter set of the construction state track graph in the current time step length and sending the control instruction to each piece of Internet of things equipment.
Optionally, the determining, by the big data server, the construction state trajectory diagram of the target construction site according to the obtained multiple sets of target transcoding data specifically includes:
acquiring calibration transcoding data updated relative to preset transcoding data in a target time period in the multiple groups of target transcoding data;
taking the updated calibration transcoding data as state diagram data to be fitted;
determining state trajectory data to be fitted according to mapping paths of the state diagram data in the multiple groups of target transcoding data and mapping paths of other calibration transcoding data except the state diagram data in the preset transcoding data;
extracting state node data from the data queues of the state diagram data and the state trajectory data;
and fitting the extracted state node data according to the time sequence order to obtain the construction state track diagram of the target construction site.
Optionally, the starting, by the big data server, a corresponding data conversion thread based on the configuration parameter to transcode the device operation data in the target data packet to obtain target transcoded data further includes:
determining an item tag corresponding to configuration pointing item information of the configuration parameters and thread matching parameters of the configuration pointing item information, wherein the thread matching parameters represent matching paths of the configuration pointing item information of the configuration parameters; the thread matching parameters at least comprise: a first matching path and a second matching path representing configuration pointing project information of the configuration parameter;
extracting a label distribution graph corresponding to the project label, wherein the label distribution graph comprises pre-configured distribution nodes, and the distribution nodes represent path nodes which are positioned on a track curve in the label distribution graph and are configured to point to project information corresponding to the project label; the distribution node includes at least: a first matching path node and a second matching path node representing configuration pointing item information corresponding to distribution information included in the tag distribution map on a trajectory curve in the tag distribution map;
according to the item tag and the thread matching parameter, searching a thread tag matched with the configuration parameter in the tag distribution map, and starting a corresponding data conversion thread based on the thread tag;
and transcoding the equipment operation data through a thread logic list corresponding to the data conversion thread to obtain the target transcoding data.
Optionally, the determining, by the big data server, the configuration parameter of the data conversion thread corresponding to the device identifier from a preset database according to the device identifier of the internet of things device included in each target data packet specifically includes:
performing packet classification on each received target data packet to determine a plurality of data packet classes of the target data packet, and performing class feature screening based on the plurality of data packet classes; the classes of the data packets are the classes corresponding to the sub data packets corresponding to the internet of things equipment in the target data packet;
performing cosine distance calculation of a category field on the target data packet category of the target data packet obtained by screening and each preset data packet category in a preset category set; the preset category set stores category configuration data corresponding to a plurality of preset data packet categories and service labels belonging to the corresponding service behavior information, wherein the preset data packet categories are verified categories of the Internet of things equipment; if the types of the target data packets of the screened target data packets are multiple, calculating the cosine distance of the type field in the following mode: performing traversal interval calculation according to the priority sequence preset for the target data packet type; in each round of interval calculation, the cosine distance of the category field is calculated only based on one of the preset data packet categories, and the preset data packet categories falling into the preset cosine distance interval are added into a list of the next round of interval calculation so as to calculate the cosine distance of the category field based on the next preset data packet category;
determining an equipment identifier to which the target data packet belongs according to a service label of the Internet of things equipment corresponding to a preset data packet type with a cosine distance of a type field of the target data packet meeting a set condition, determining a plurality of initial configuration parameters from a preset database according to the equipment identifier, and determining configuration parameters of a data conversion thread corresponding to the equipment identifier from the plurality of initial configuration parameters based on an encapsulation protocol of the target data packet.
Optionally, the iteratively monitoring the construction state trajectory diagram by the big data server according to a set time step length to determine whether the construction state trajectory diagram is abnormal specifically includes:
determining a state change vector of the construction state track graph and each construction service data according to the set time step;
if the construction state track graph contains the self-adaptive correction label based on the state change vector, calculating business connection behavior values between each construction business data of the construction state track graph under the cured data label and each construction business data of the construction state track graph under the self-adaptive correction label according to the construction business data of the construction state track graph under the self-adaptive correction label and the effective construction duration of the construction business data;
capturing construction business data of the construction state trajectory graph under the solidified data label and associated with the construction business data under the adaptive correction label based on the business contact behavior value; if the solidified data label of the construction state track graph contains a plurality of construction service data, calculating a service connection behavior value between each piece of construction service data under the solidified data label of the construction state track graph based on the construction service data under the adaptive correction label of the construction state track graph and the effective construction duration of the construction service data, and then filtering each piece of construction service data under the solidified data label according to the service connection behavior value between each piece of construction service data; capturing the target construction service data remained after the filtering to the adaptive correction label according to the construction service data of the construction state trajectory diagram under the adaptive correction label and the effective construction duration of the construction service data;
periodically calculating a data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; when the matrix characteristic value of the data difference matrix does not exceed a set value, judging that the construction state track graph is not abnormal, and returning to the step of periodically calculating the data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; and judging that the construction state track graph is abnormal when the matrix characteristic value of the data difference matrix exceeds the set value.
Advantageous effects
According to the method and system for visualizing the Internet of things big data based on the intelligent construction site, the Internet of things equipment uploads a packaged running data packet to a big data server, the big data server determines configuration parameters of a data conversion thread corresponding to equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet to start the corresponding data conversion thread so as to transcode the equipment running data to obtain target transcoding data, then a construction track state diagram is obtained according to the target transcoding data and displayed visually, and finally a track parameter set in the current time step generates a control instruction and issues the control instruction when the abnormality of the construction state track diagram is monitored. Therefore, monitoring delay can be reduced and the visualization of the monitoring data can be realized by transcoding and then analyzing the equipment operation data.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a communication architecture diagram of an internet of things big data visualization system based on an intelligent construction site in the scheme.
Fig. 2 is a schematic flow chart of a method for visualizing big data of the internet of things based on an intelligent construction site in the scheme.
Fig. 3 is a schematic diagram of a hardware structure of a big data server in the present solution.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The inventor researches and analyzes technical problems occurring in the background art, and finds that the technical problems occur because a barrier for data interaction exists between various internet of things devices in a smart site (for example, device operation data between different internet of things devices are incompatible), which results in that time consumption for analyzing various device operation data is increased, and thus the generation and issuing cycle of a monitoring strategy is prolonged.
In order to solve the technical problems, the embodiment of the invention provides an internet of things big data visualization method and system based on an intelligent construction site. Referring first to fig. 1, the present invention provides a communication architecture diagram of an internet of things big data visualization system 100 based on an intelligent worksite, where the internet of things big data visualization system 100 may include a big data server 110 and a plurality of internet of things devices 120, the big data server 110 is in communication with each internet of things device 120, and the plurality of internet of things devices 120 are deployed in a target worksite. In this embodiment, the internet of things device 120 may be an access control device, a monitoring camera, a construction device, and the like, which is not limited herein. Further, referring to fig. 2, the present invention provides a flow chart of a method for visualizing big data of internet of things based on an intelligent construction site, where the method may be applied to the system 100 for visualizing big data of internet of things shown in fig. 1, and specifically may include the contents described in the following steps S210 to S240.
Step S210, each internet of things device periodically encapsulates the corresponding device operation data to obtain an operation data packet, and uploads the operation data packet to the big data server through a data transmission channel pre-established with the big data server.
In step S210, the data transmission channels corresponding to different internet of things devices 120 are different, so that transmission delay or data loss caused by the fact that the same data transmission channel transmits multiple sets of operation data packets simultaneously can be avoided.
Step S220, the big data server receives a target data packet uploaded by each Internet of things device; determining configuration parameters of a data conversion thread corresponding to the equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet, and starting the corresponding data conversion thread based on the configuration parameters to transcode equipment operation data in the target data packet to obtain target transcoding data.
In this embodiment, the preset database is used to store configuration parameters of data conversion threads corresponding to different device identifiers, and the configuration parameters are used to start different data conversion threads. The number of data conversion threads in this embodiment may be plural. Further, the data formats of different target transcoding data are consistent, so that the target transcoding data can be accurately and uniformly processed subsequently.
And step S230, the big data server determines a construction state track graph of the target construction site according to the obtained multiple groups of target transcoding data and displays the construction state track graph in real time.
In the embodiment, due to the fact that different equipment operation data are transcoded, compatibility and data relevance among target transcoding data can be considered when multiple groups of target transcoding data are analyzed, and therefore integration of the target transcoding data is achieved, and the construction track state diagram is accurately and completely determined and visually displayed.
Step S240, carrying out iterative monitoring on the construction state track graph according to a set time step length to determine whether the construction state track graph is abnormal or not; and when monitoring that the construction state track graph is abnormal, generating a control instruction according to the track parameter set of the construction state track graph in the current time step length and sending the control instruction to each piece of Internet of things equipment.
In step S240, the set time step may be adjusted according to the number of the internet of things devices 120 communicating with the big data server 110, which is not limited herein.
It can be understood that, through the steps S210 to S240, the internet of things device first uploads the packaged operation data packets to the big data server, the big data server then determines configuration parameters of a data conversion thread corresponding to the device identifier from a preset database according to the device identifier of the internet of things device included in each target data packet, starts the corresponding data conversion thread to transcode the device operation data to obtain target transcoding data, then obtains a construction track state diagram according to the target transcoding data and performs visual display, and finally generates and issues a control instruction according to a track parameter set within a current time step when it is monitored that the construction state track diagram is abnormal. Therefore, monitoring delay can be reduced and the visualization of the monitoring data can be realized by transcoding and then analyzing the equipment operation data.
In particular, the inventor found that the configuration parameters may not match the device operating data in the target data packet when determining the configuration parameters. The reason for this is that certain similarity exists between configuration parameters, which may cause a mis-selection when determining the configuration parameters. To solve the technical problem, in step S220, the configuration parameters of the data conversion thread corresponding to the device identifier are determined from the preset database according to the device identifier of the internet of things device included in each target data packet, which may specifically include the contents described in steps S2211 to S2213 below.
Step S2211, performing packet classification on each received target packet to determine multiple packet classes of the target packet, and performing class feature screening based on the multiple packet classes; the data packet types are types corresponding to sub data packets corresponding to the internet of things equipment in the target data packet.
Step S2212, performing cosine distance calculation of category fields on the target data packet category of the target data packet obtained by screening and each preset data packet category in a preset category set; the preset category set stores category configuration data corresponding to a plurality of preset data packet categories and service labels belonging to the corresponding service behavior information, wherein the preset data packet categories are verified categories of the Internet of things equipment; if the types of the target data packets of the screened target data packets are multiple, calculating the cosine distance of the type field in the following mode: performing traversal interval calculation according to the priority sequence preset for the target data packet type; in each round of interval calculation, the cosine distance of the category field is calculated only based on one of the preset data packet categories, and the preset data packet categories falling into the preset cosine distance interval are added into a list of the next round of interval calculation, so that the cosine distance of the category field is calculated based on the next preset data packet category.
Step S2213, determining an equipment identifier to which the target data packet belongs according to a service tag of the internet of things equipment corresponding to a preset data packet category whose cosine distance from the category field of the target data packet meets a set condition, determining a plurality of initial configuration parameters from a preset database according to the equipment identifier, and determining a configuration parameter of a data conversion thread corresponding to the equipment identifier from the plurality of initial configuration parameters based on an encapsulation protocol of the target data packet.
It is understood that based on the descriptions of step S2211 to step S2213, the configuration parameters can be determined to ensure the matching of the configuration parameters with the device operation data in the target data packet.
Further, in order to ensure that data is not damaged or scrambled during data transcoding, the method described in step S220, which starts a corresponding data conversion thread based on the configuration parameters to transcode the device operation data in the target data packet to obtain target transcoded data, may further include the following steps S2221 to S2224.
Step S2221, determining an item tag corresponding to configuration-oriented item information of the configuration parameters and thread matching parameters of the configuration-oriented item information, wherein the thread matching parameters represent matching paths of the configuration-oriented item information of the configuration parameters; the thread matching parameters at least comprise: the configuration representing the configuration parameters points to a first matching path and a second matching path of project information.
Step S2222, extracting a label distribution map corresponding to the item label, wherein the label distribution map comprises pre-configured distribution nodes, and the distribution nodes represent path nodes which are positioned on a trajectory curve in the label distribution map and are configured to point to item information corresponding to the item label; the distribution node includes at least: and a first matching path node and a second matching path node indicating that the arrangement of the pointing item information corresponding to the distribution information included in the tag distribution map is on the trajectory curve in the tag distribution map.
Step S2223, according to the item tag and the thread matching parameter, finding the thread tag matched with the configuration parameter in the tag distribution map, and starting the corresponding data conversion thread based on the thread tag.
And step S2224, transcoding the device operation data through the thread logic list corresponding to the data conversion thread to obtain the target transcoding data.
Thus, based on the steps S2221 to S2224, it can be ensured that data is not damaged or scrambled during data transcoding.
In practical application, in order to ensure that the construction state track graph can completely and comprehensively reflect the construction progress of the target construction site, in step S230, the big data server determines the construction state track graph of the target construction site according to the obtained multiple sets of target transcoding data, which may exemplarily include the contents described in the following steps S231-S235.
Step S231, obtaining calibration transcoding data, which is updated with respect to preset transcoding data in a target time period, in the multiple sets of target transcoding data.
And step S232, taking the updated calibration transcoding data as state diagram data to be fitted.
Step S233, determining the state trajectory data to be fitted according to the mapping paths of the state diagram data in the multiple sets of target transcoding data and the mapping paths of other calibration transcoding data in the preset transcoding data except the state diagram data.
Step S234, extracting state node data from the data queues of the state diagram data and the state trace data.
And step S235, fitting the extracted state node data according to a time sequence to obtain a construction state track graph of the target construction site.
When the steps S231 to S235 are applied, it can be ensured that the construction state trajectory diagram can completely and comprehensively reflect the construction progress of the target construction site.
In one possible embodiment, in order to achieve accurate monitoring and management of the target worksite, the step S240 may specifically include the following steps S2411 to S2414, which are described in detail in order to perform iterative monitoring on the construction state track diagram according to the set time step to determine whether the construction state track diagram is abnormal.
Step S2411, determining the state change vector of the construction state track graph and each construction business data according to the set time step.
Step S2412, if the construction state trajectory diagram contains the self-adaptive correction label based on the state change vector, calculating the business connection behavior value between each construction business data of the construction state trajectory diagram under the solidified data label and each construction business data of the construction state trajectory diagram under the self-adaptive correction label according to the construction business data of the construction state trajectory diagram under the self-adaptive correction label and the effective construction duration of the construction business data.
Step S2413, capturing construction service data of the construction state trajectory graph under the solidified data label and related to the construction service data under the adaptive correction label to the adaptive correction label based on the service contact behavior value; if the solidified data label of the construction state track graph contains a plurality of construction service data, calculating a service connection behavior value between each piece of construction service data under the solidified data label of the construction state track graph based on the construction service data under the adaptive correction label of the construction state track graph and the effective construction duration of the construction service data, and then filtering each piece of construction service data under the solidified data label according to the service connection behavior value between each piece of construction service data; capturing the target construction service data remained after the filtering to the adaptive correction label according to the construction service data of the construction state trajectory diagram under the adaptive correction label and the effective construction duration of the construction service data.
Step S2414, periodically calculating a data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; when the matrix characteristic value of the data difference matrix does not exceed a set value, judging that the construction state track graph is not abnormal, and returning to the step of periodically calculating the data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; and judging that the construction state track graph is abnormal when the matrix characteristic value of the data difference matrix exceeds the set value.
Through the steps S2411 to S2414, accurate monitoring and management of the target construction site can be realized through periodic calculation of the data difference matrix, so that the construction abnormity of the target construction site can be judged in time.
In an alternative embodiment, in order to implement accurate control and management on the internet of things devices, in step S240, a control instruction is generated according to the trajectory parameter set of the construction state trajectory diagram in the current time step and is issued to each internet of things device, which may specifically include the contents described in the following steps S2421 to S2424.
Step S2421, collecting first parameter variables of the track parameter set in advance, wherein the first parameter variables comprise equipment cooperation variables and equipment adjustment variables; and acquiring an equipment cooperation list corresponding to the equipment cooperation variables and an adjustment track list corresponding to the equipment adjustment variables.
Step S2422, generating and storing a first instruction code based on the equipment cooperation list and the adjustment track list; acquiring a second parameter variable of the track parameter set and extracting a first variable characteristic and a second variable characteristic in the second parameter variable; wherein the first variable characteristic is used for characterizing equipment adjustment variables of a track change queue of the track parameter set; the second variable characteristic is used for representing the equipment cooperative variable of the track change queue of the track parameter set.
Step S2423, obtaining a third variable characteristic based on the first variable characteristic and the second variable characteristic, and generating a second instruction code according to the third variable characteristic.
Step S2424, generating a control instruction according to the difference sequence of the first instruction code and the second instruction code in the time sequence, and issuing the control instruction to each Internet of things device.
It can be understood that, through the content described in the above steps S2421 to S2424, precise control and management of the internet of things device can be achieved.
Based on the same inventive concept, the internet of things big data visualization system based on the intelligent construction site comprises a big data server and a plurality of internet of things devices, wherein the big data server is communicated with the plurality of internet of things devices, and the plurality of internet of things devices are deployed in the target construction site;
the Internet of things equipment is used for:
periodically packaging corresponding equipment operation data to obtain an operation data packet, and uploading the operation data packet to the big data server through a data transmission channel pre-established with the big data server;
the big data server is used for:
receiving a target data packet uploaded by each Internet of things device; determining configuration parameters of a data conversion thread corresponding to the equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet, and starting the corresponding data conversion thread based on the configuration parameters to transcode equipment operation data in the target data packet to obtain target transcoding data;
determining a construction state track graph of the target construction site according to the obtained multiple groups of target transcoding data and displaying the construction state track graph in real time;
performing iterative monitoring on the construction state track graph according to a set time step length to determine whether the construction state track graph is abnormal or not; and when monitoring that the construction state track graph is abnormal, generating a control instruction according to the track parameter set of the construction state track graph in the current time step length and sending the control instruction to each piece of Internet of things equipment.
Optionally, the determining, by the big data server, the construction state trajectory diagram of the target construction site according to the obtained multiple sets of target transcoding data specifically includes:
acquiring calibration transcoding data updated relative to preset transcoding data in a target time period in the multiple groups of target transcoding data;
taking the updated calibration transcoding data as state diagram data to be fitted;
determining state trajectory data to be fitted according to mapping paths of the state diagram data in the multiple groups of target transcoding data and mapping paths of other calibration transcoding data except the state diagram data in the preset transcoding data;
extracting state node data from the data queues of the state diagram data and the state trajectory data;
and fitting the extracted state node data according to the time sequence order to obtain the construction state track diagram of the target construction site.
Optionally, the starting, by the big data server, a corresponding data conversion thread based on the configuration parameter to transcode the device operation data in the target data packet to obtain target transcoded data further includes:
determining an item tag corresponding to configuration pointing item information of the configuration parameters and thread matching parameters of the configuration pointing item information, wherein the thread matching parameters represent matching paths of the configuration pointing item information of the configuration parameters; the thread matching parameters at least comprise: a first matching path and a second matching path representing configuration pointing project information of the configuration parameter;
extracting a label distribution graph corresponding to the project label, wherein the label distribution graph comprises pre-configured distribution nodes, and the distribution nodes represent path nodes which are positioned on a track curve in the label distribution graph and are configured to point to project information corresponding to the project label; the distribution node includes at least: a first matching path node and a second matching path node representing configuration pointing item information corresponding to distribution information included in the tag distribution map on a trajectory curve in the tag distribution map;
according to the item tag and the thread matching parameter, searching a thread tag matched with the configuration parameter in the tag distribution map, and starting a corresponding data conversion thread based on the thread tag;
and transcoding the equipment operation data through a thread logic list corresponding to the data conversion thread to obtain the target transcoding data.
Optionally, the determining, by the big data server, the configuration parameter of the data conversion thread corresponding to the device identifier from a preset database according to the device identifier of the internet of things device included in each target data packet specifically includes:
performing packet classification on each received target data packet to determine a plurality of data packet classes of the target data packet, and performing class feature screening based on the plurality of data packet classes; the classes of the data packets are the classes corresponding to the sub data packets corresponding to the internet of things equipment in the target data packet;
performing cosine distance calculation of a category field on the target data packet category of the target data packet obtained by screening and each preset data packet category in a preset category set; the preset category set stores category configuration data corresponding to a plurality of preset data packet categories and service labels belonging to the corresponding service behavior information, wherein the preset data packet categories are verified categories of the Internet of things equipment; if the types of the target data packets of the screened target data packets are multiple, calculating the cosine distance of the type field in the following mode: performing traversal interval calculation according to the priority sequence preset for the target data packet type; in each round of interval calculation, the cosine distance of the category field is calculated only based on one of the preset data packet categories, and the preset data packet categories falling into the preset cosine distance interval are added into a list of the next round of interval calculation so as to calculate the cosine distance of the category field based on the next preset data packet category;
determining an equipment identifier to which the target data packet belongs according to a service label of the Internet of things equipment corresponding to a preset data packet type with a cosine distance of a type field of the target data packet meeting a set condition, determining a plurality of initial configuration parameters from a preset database according to the equipment identifier, and determining configuration parameters of a data conversion thread corresponding to the equipment identifier from the plurality of initial configuration parameters based on an encapsulation protocol of the target data packet.
Optionally, the iteratively monitoring the construction state trajectory diagram by the big data server according to a set time step length to determine whether the construction state trajectory diagram is abnormal specifically includes:
determining a state change vector of the construction state track graph and each construction service data according to the set time step;
if the construction state track graph contains the self-adaptive correction label based on the state change vector, calculating business connection behavior values between each construction business data of the construction state track graph under the cured data label and each construction business data of the construction state track graph under the self-adaptive correction label according to the construction business data of the construction state track graph under the self-adaptive correction label and the effective construction duration of the construction business data;
capturing construction business data of the construction state trajectory graph under the solidified data label and associated with the construction business data under the adaptive correction label based on the business contact behavior value; if the solidified data label of the construction state track graph contains a plurality of construction service data, calculating a service connection behavior value between each piece of construction service data under the solidified data label of the construction state track graph based on the construction service data under the adaptive correction label of the construction state track graph and the effective construction duration of the construction service data, and then filtering each piece of construction service data under the solidified data label according to the service connection behavior value between each piece of construction service data; capturing the target construction service data remained after the filtering to the adaptive correction label according to the construction service data of the construction state trajectory diagram under the adaptive correction label and the effective construction duration of the construction service data;
periodically calculating a data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; when the matrix characteristic value of the data difference matrix does not exceed a set value, judging that the construction state track graph is not abnormal, and returning to the step of periodically calculating the data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; and judging that the construction state track graph is abnormal when the matrix characteristic value of the data difference matrix exceeds the set value.
On the basis of the above, please refer to fig. 3 in combination, the present invention provides a hardware structure diagram of a big data server 110, where the big data server 110 includes a processor 111 and a memory 112 that communicate with each other, and the processor 111 executes the computer program called from the memory 112 to execute the method shown in fig. 2.
To sum up, according to the method and system for visualizing the internet of things big data based on the intelligent construction site provided by the embodiment of the invention, the internet of things equipment uploads the packaged operation data packets to the big data server, the big data server determines the configuration parameters of the data conversion thread corresponding to the equipment identification from the preset database according to the equipment identification of the internet of things equipment in each target data packet, starts the corresponding data conversion thread to transcode the equipment operation data to obtain target transcoding data, obtains the construction track state diagram according to the target transcoding data and performs visual display, and finally generates and issues the control instruction according to the track parameter set in the current time step when the abnormality of the construction state track diagram is monitored. Therefore, monitoring delay can be reduced and the visualization of the monitoring data can be realized by transcoding and then analyzing the equipment operation data.
It is to be understood that the present invention is not limited to what has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (8)
1. An Internet of things big data visualization method based on an intelligent construction site is applied to a big data server and a plurality of Internet of things devices deployed in a target construction site, and comprises the following steps:
each piece of Internet of things equipment periodically packages corresponding equipment operation data to obtain an operation data packet, and uploads the operation data packet to the big data server through a data transmission channel pre-established with the big data server;
the big data server receives a target data packet uploaded by each Internet of things device; determining configuration parameters of a data conversion thread corresponding to the equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet, and starting the corresponding data conversion thread based on the configuration parameters to transcode equipment operation data in the target data packet to obtain target transcoding data;
the big data server determines a construction state track graph of the target construction site according to the obtained multiple groups of target transcoding data and displays the construction state track graph in real time;
performing iterative monitoring on the construction state track graph according to a set time step length to determine whether the construction state track graph is abnormal or not; when the construction state track graph is monitored to be abnormal, generating a control instruction according to a track parameter set of the construction state track graph in the current time step length and sending the control instruction to each piece of Internet of things equipment;
starting a corresponding data conversion thread based on the configuration parameters to transcode the device operation data in the target data packet to obtain target transcoding data, further comprising: determining an item tag corresponding to configuration pointing item information of the configuration parameters and thread matching parameters of the configuration pointing item information, wherein the thread matching parameters represent matching paths of the configuration pointing item information of the configuration parameters; the thread matching parameters at least comprise: a first matching path and a second matching path representing configuration pointing project information of the configuration parameter; extracting a label distribution graph corresponding to the project label, wherein the label distribution graph comprises pre-configured distribution nodes, and the distribution nodes represent path nodes which are positioned on a track curve in the label distribution graph and are configured to point to project information corresponding to the project label; the distribution node includes at least: a first matching path node and a second matching path node representing configuration pointing item information corresponding to distribution information included in the tag distribution map on a trajectory curve in the tag distribution map; according to the item tag and the thread matching parameter, searching a thread tag matched with the configuration parameter in the tag distribution map, and starting a corresponding data conversion thread based on the thread tag; and transcoding the equipment operation data through a thread logic list corresponding to the data conversion thread to obtain the target transcoding data.
2. The method of claim 1, wherein the big data server determines a construction state trajectory map of the target worksite from the obtained sets of target transcoding data, comprising:
acquiring calibration transcoding data updated relative to preset transcoding data in a target time period in the multiple groups of target transcoding data;
taking the updated calibration transcoding data as state diagram data to be fitted;
determining state trajectory data to be fitted according to mapping paths of the state diagram data in the multiple groups of target transcoding data and mapping paths of other calibration transcoding data except the state diagram data in the preset transcoding data;
extracting state node data from the data queues of the state diagram data and the state trajectory data;
and fitting the extracted state node data according to the time sequence order to obtain the construction state track diagram of the target construction site.
3. The method according to claim 1, wherein determining, from a preset database, configuration parameters of a data conversion thread corresponding to the device identifier according to the device identifier of the internet of things device included in each target data packet specifically includes:
performing packet classification on each received target data packet to determine a plurality of data packet classes of the target data packet, and performing class feature screening based on the plurality of data packet classes; the classes of the data packets are the classes corresponding to the sub data packets corresponding to the internet of things equipment in the target data packet;
performing cosine distance calculation of a category field on the target data packet category of the target data packet obtained by screening and each preset data packet category in a preset category set; the preset category set stores category configuration data corresponding to a plurality of preset data packet categories and service labels belonging to the corresponding service behavior information, wherein the preset data packet categories are verified categories of the Internet of things equipment; if the types of the target data packets of the screened target data packets are multiple, calculating the cosine distance of the type field in the following mode: performing traversal interval calculation according to the priority sequence preset for the target data packet type; in each round of interval calculation, the cosine distance of the category field is calculated only based on one of the preset data packet categories, and the preset data packet categories falling into the preset cosine distance interval are added into a list of the next round of interval calculation so as to calculate the cosine distance of the category field based on the next preset data packet category;
determining an equipment identifier to which the target data packet belongs according to a service label of the Internet of things equipment corresponding to a preset data packet type with a cosine distance of a type field of the target data packet meeting a set condition, determining a plurality of initial configuration parameters from a preset database according to the equipment identifier, and determining configuration parameters of a data conversion thread corresponding to the equipment identifier from the plurality of initial configuration parameters based on an encapsulation protocol of the target data packet.
4. The method of claim 1, wherein iteratively monitoring the construction state trajectory graph according to a set time step to determine whether the construction state trajectory graph is abnormal comprises:
determining a state change vector of the construction state track graph and each construction service data according to the set time step;
if the construction state track graph contains the self-adaptive correction label based on the state change vector, calculating business connection behavior values between each construction business data of the construction state track graph under the cured data label and each construction business data of the construction state track graph under the self-adaptive correction label according to the construction business data of the construction state track graph under the self-adaptive correction label and the effective construction duration of the construction business data;
capturing construction business data of the construction state trajectory graph under the solidified data label and associated with the construction business data under the adaptive correction label based on the business contact behavior value; if the solidified data label of the construction state track graph contains a plurality of construction service data, calculating a service connection behavior value between each piece of construction service data under the solidified data label of the construction state track graph based on the construction service data under the adaptive correction label of the construction state track graph and the effective construction duration of the construction service data, and then filtering each piece of construction service data under the solidified data label according to the service connection behavior value between each piece of construction service data; capturing the target construction service data remained after the filtering to the adaptive correction label according to the construction service data of the construction state trajectory diagram under the adaptive correction label and the effective construction duration of the construction service data;
periodically calculating a data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; when the matrix characteristic value of the data difference matrix does not exceed a set value, judging that the construction state track graph is not abnormal, and returning to the step of periodically calculating the data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; and judging that the construction state track graph is abnormal when the matrix characteristic value of the data difference matrix exceeds the set value.
5. An Internet of things big data visualization system based on an intelligent construction site is characterized by comprising a big data server and a plurality of Internet of things devices, wherein the big data server is communicated with the plurality of Internet of things devices, and the plurality of Internet of things devices are deployed in a target construction site;
the Internet of things equipment is used for:
periodically packaging corresponding equipment operation data to obtain an operation data packet, and uploading the operation data packet to the big data server through a data transmission channel pre-established with the big data server;
the big data server is used for:
receiving a target data packet uploaded by each Internet of things device; determining configuration parameters of a data conversion thread corresponding to the equipment identification from a preset database according to the equipment identification of the Internet of things equipment in each target data packet, and starting the corresponding data conversion thread based on the configuration parameters to transcode equipment operation data in the target data packet to obtain target transcoding data;
determining a construction state track graph of the target construction site according to the obtained multiple groups of target transcoding data and displaying the construction state track graph in real time;
performing iterative monitoring on the construction state track graph according to a set time step length to determine whether the construction state track graph is abnormal or not; when the construction state track graph is monitored to be abnormal, generating a control instruction according to a track parameter set of the construction state track graph in the current time step length and sending the control instruction to each piece of Internet of things equipment;
the big data server starts a corresponding data conversion thread based on the configuration parameters to transcode the equipment operation data in the target data packet to obtain target transcoding data further comprises:
determining an item tag corresponding to configuration pointing item information of the configuration parameters and thread matching parameters of the configuration pointing item information, wherein the thread matching parameters represent matching paths of the configuration pointing item information of the configuration parameters; the thread matching parameters at least comprise: a first matching path and a second matching path representing configuration pointing project information of the configuration parameter;
extracting a label distribution graph corresponding to the project label, wherein the label distribution graph comprises pre-configured distribution nodes, and the distribution nodes represent path nodes which are positioned on a track curve in the label distribution graph and are configured to point to project information corresponding to the project label; the distribution node includes at least: a first matching path node and a second matching path node representing configuration pointing item information corresponding to distribution information included in the tag distribution map on a trajectory curve in the tag distribution map;
according to the item tag and the thread matching parameter, searching a thread tag matched with the configuration parameter in the tag distribution map, and starting a corresponding data conversion thread based on the thread tag;
and transcoding the equipment operation data through a thread logic list corresponding to the data conversion thread to obtain the target transcoding data.
6. The system of claim 5, wherein the big data server determining the construction state trajectory diagram of the target worksite from the obtained sets of target transcoding data specifically comprises:
acquiring calibration transcoding data updated relative to preset transcoding data in a target time period in the multiple groups of target transcoding data;
taking the updated calibration transcoding data as state diagram data to be fitted;
determining state trajectory data to be fitted according to mapping paths of the state diagram data in the multiple groups of target transcoding data and mapping paths of other calibration transcoding data except the state diagram data in the preset transcoding data;
extracting state node data from the data queues of the state diagram data and the state trajectory data;
and fitting the extracted state node data according to the time sequence order to obtain the construction state track diagram of the target construction site.
7. The system according to claim 5, wherein the determining, by the big data server, the configuration parameter of the data conversion thread corresponding to the device identifier from a preset database according to the device identifier of the internet of things device included in each target data packet specifically includes:
performing packet classification on each received target data packet to determine a plurality of data packet classes of the target data packet, and performing class feature screening based on the plurality of data packet classes; the classes of the data packets are the classes corresponding to the sub data packets corresponding to the internet of things equipment in the target data packet;
performing cosine distance calculation of a category field on the target data packet category of the target data packet obtained by screening and each preset data packet category in a preset category set; the preset category set stores category configuration data corresponding to a plurality of preset data packet categories and service labels belonging to the corresponding service behavior information, wherein the preset data packet categories are verified categories of the Internet of things equipment; if the types of the target data packets of the screened target data packets are multiple, calculating the cosine distance of the type field in the following mode: performing traversal interval calculation according to the priority sequence preset for the target data packet type; in each round of interval calculation, the cosine distance of the category field is calculated only based on one of the preset data packet categories, and the preset data packet categories falling into the preset cosine distance interval are added into a list of the next round of interval calculation so as to calculate the cosine distance of the category field based on the next preset data packet category;
determining an equipment identifier to which the target data packet belongs according to a service label of the Internet of things equipment corresponding to a preset data packet type with a cosine distance of a type field of the target data packet meeting a set condition, determining a plurality of initial configuration parameters from a preset database according to the equipment identifier, and determining configuration parameters of a data conversion thread corresponding to the equipment identifier from the plurality of initial configuration parameters based on an encapsulation protocol of the target data packet.
8. The system of claim 5, wherein the big data server iteratively monitors the construction state trajectory graph according to a set time step to determine whether the construction state trajectory graph is abnormal specifically comprises:
determining a state change vector of the construction state track graph and each construction service data according to the set time step;
if the construction state track graph contains the self-adaptive correction label based on the state change vector, calculating business connection behavior values between each construction business data of the construction state track graph under the cured data label and each construction business data of the construction state track graph under the self-adaptive correction label according to the construction business data of the construction state track graph under the self-adaptive correction label and the effective construction duration of the construction business data;
capturing construction business data of the construction state trajectory graph under the solidified data label and associated with the construction business data under the adaptive correction label based on the business contact behavior value; if the solidified data label of the construction state track graph contains a plurality of construction service data, calculating a service connection behavior value between each piece of construction service data under the solidified data label of the construction state track graph based on the construction service data under the adaptive correction label of the construction state track graph and the effective construction duration of the construction service data, and then filtering each piece of construction service data under the solidified data label according to the service connection behavior value between each piece of construction service data; capturing the target construction service data remained after the filtering to the adaptive correction label according to the construction service data of the construction state trajectory diagram under the adaptive correction label and the effective construction duration of the construction service data;
periodically calculating a data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; when the matrix characteristic value of the data difference matrix does not exceed a set value, judging that the construction state track graph is not abnormal, and returning to the step of periodically calculating the data difference matrix between the construction service data under the self-adaptive correction label and the construction service data under the solidified data label; and judging that the construction state track graph is abnormal when the matrix characteristic value of the data difference matrix exceeds the set value.
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