CN118761635B - A computing and communication device based on artificial intelligence hardware collaboration - Google Patents
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Abstract
The invention provides a calculation and communication device based on artificial intelligence and hardware cooperation, which belongs to the technical field of communication devices, and comprises a communication module and a calculation module, wherein the calculation module is applied to a building site risk identification method and specifically comprises the following steps: when the calculation and communication device is used as an analysis device and the monitoring device is determined based on the construction risk factors, the analysis device is not required to be independently arranged, the monitoring device is used as a general device, the general device is divided into different monitoring device combinations by utilizing the monitoring positions, the construction risk factors of different general devices in the monitoring device combinations and the distances between the monitoring device combinations and the analysis device are used for determining the optimal setting scheme in the optimal setting scheme, and the optimal setting scheme is used for carrying out the local identification processing of the construction risk of the monitoring device combinations, so that the efficiency of the identification processing of the construction safety risk is ensured.
Description
Technical Field
The invention belongs to the technical field of communication devices, and particularly relates to a computing and communication device based on artificial intelligence hardware cooperation.
Background
In order to realize safety supervision on a building site, in the prior art scheme, safety risk identification is often performed through the setting of an internet of things monitoring device, and in the invention patent application CN202410143900.5, namely, the intelligent building site safety supervision method and system based on the internet of things, the safety risk is identified through the internet of things monitoring device, but the following technical problems exist:
the real-time requirement of the safety risk identification of the construction site is very high, and once the safety risk is not intervened in time, the safety risk identification can possibly develop and extend into larger safety accidents, so that if the safety risk identification processing cannot be performed by utilizing a local cooperative computing device, the safety risk identification processing efficiency cannot be ensured.
Aiming at the technical problems, the invention provides a computing and communication device based on artificial intelligence hardware cooperation.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
According to one aspect of the present invention, a computing and communication device based on artificial intelligence hardware collaboration is provided.
The utility model provides a calculation and communication device based on artificial intelligence hardware cooperation, has communication module, calculation module is applied to a building site risk identification method, specifically includes:
S1, determining construction risk factors of different monitoring devices based on distribution data of risk points of a monitoring area of the monitoring device, taking a computing and communication device as an analysis device, and taking the monitoring device as a general device when the monitoring device is determined to be not required to be independently set by the analysis device based on the construction risk factors;
s2, dividing the general devices into different monitoring device combinations by using the monitoring positions, and determining the set number of analysis devices of the monitoring device combinations based on the distances between the general devices in the monitoring device combinations and construction risk factors of the different general devices;
S3, taking the monitoring positions and the set number of the general devices as constraint conditions, generating a set scheme of an analysis device of the monitoring device combination, and determining a preferable set scheme in the scheme based on the distance between the analysis devices;
S4, determining an optimal setting scheme in the optimal setting schemes according to construction risk factors of different general devices in the monitoring device combination and the distance between the construction risk factors and the analysis device, and carrying out local identification processing of the construction risk of the monitoring device combination by utilizing the optimal setting scheme.
The invention has the beneficial effects that:
1. The method has the advantages that the set number of the analysis devices of the monitoring device combination is determined based on the distance between the general devices in the monitoring device combination and the construction risk factors of different general devices, namely, the difference of communication processing difficulty when the general devices perform risk identification processing due to the difference of the distance between the general devices is ensured, meanwhile, the requirement of risk identification of the monitoring device combination with larger construction risk is also ensured by further combining the construction risk factors of the general devices, and on the basis of reducing the cost as much as possible, the requirement of identification processing of the construction safety risk of the monitoring device combination is also met.
2. The construction risk factors of different general devices in the monitoring device combination and the optimal setting scheme in the optimal setting scheme are determined according to the distance between the construction risk factors and the analysis device, so that the accurate setting of the analysis device from multiple dimensions is realized, the technical problem of large communication difficulty in risk identification caused by the fact that the distance between the construction risk factors and the general devices is long is avoided, and the reliability of the identification processing of the construction safety risk of the monitoring device combination is improved through the layout optimization of the setting positions on the basis that the setting number of the analysis devices is not increased.
The further technical scheme is that the communication module is responsible for communication processing with the monitoring device, other analysis devices and the unified safety monitoring platform.
The further technical scheme is that the computing module is responsible for carrying out construction safety risk identification processing on monitoring data of the monitoring device by using a preset artificial intelligent model.
A further technical solution is that the distribution data of the risk points comprise the number of different types of risk points and the distribution position in the monitoring area.
The further technical scheme is that the method for determining the optimal setting scheme in the optimal setting scheme comprises the following steps:
Determining the risk identification reliability of different general devices based on the distance between the different general devices and the different analysis devices;
determining weight coefficients of risk identification reliability of different general devices by using construction risk factors of different general devices;
and determining the identification reliability of the priority setting scheme by the weight coefficient and the risk identification reliability of different general devices, and determining the optimal setting scheme in the optimal setting scheme according to the identification reliability.
The further technical scheme is that the optimal setting scheme is the optimal setting scheme with the maximum identification reliability.
The further technical scheme is that the local identification processing of the construction risk of the monitoring device combination is performed, and specifically comprises the following steps:
Local identification processing of construction safety risks of the monitoring device combination by utilizing the optimal setting scheme
And setting the analysis device in the monitoring device combination by using the optimal setting scheme, carrying out monitoring data of the monitoring devices in the monitoring device combination according to the analysis device in the monitoring device combination, and carrying out safety risk identification processing according to a preset artificial intelligent model.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention as set forth hereinafter.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings;
FIG. 1 is a flow chart of a computing and communication device based on artificial intelligence hardware collaboration;
FIG. 2 is a flow chart of a method of monitoring a determination of a construction risk factor for a device;
FIG. 3 is a flow chart of a method of monitoring a determination of a set number of analysis devices in a device combination;
fig. 4 is a flow chart of a method of a preferred setup scenario among the scenarios.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
Examples
In order to solve the above problems, according to one aspect of the present invention, as shown in fig. 1, there is provided a computing and communication device based on cooperation of artificial intelligence hardware, which has a communication module, a computing module, and a risk recognition method applied to a construction site, wherein the method specifically includes:
S1, determining construction risk factors of different monitoring devices based on distribution data of risk points of a monitoring area of the monitoring device, taking a computing and communication device as an analysis device, and taking the monitoring device as a general device when the monitoring device is determined to be not required to be independently set by the analysis device based on the construction risk factors;
furthermore, the communication module is responsible for communication processing with the monitoring device, other analysis devices and the unified safety monitoring platform.
Specifically, the computing module is responsible for carrying out identification processing of construction safety risks of monitoring data of the monitoring device by using a preset artificial intelligent model.
It will be appreciated that the distribution data of risk points includes the number of different types of risk points and the location of the distribution in the monitored area.
Specifically, as shown in fig. 2, the method for determining the construction risk factor of the monitoring device includes:
Based on the distribution data of the risk points, determining the number of different types of risk points of the monitoring device, and determining a basic risk coefficient of the monitoring device by combining preset risk coefficients of different types of risk points;
determining distribution positions of different types of risk points in the monitoring area based on distribution data of different types of risk points, determining risk sub-areas with cross construction risks through the distribution positions, and determining sub-area risk coefficients of the risk sub-areas according to the number of the risk points in the different risk sub-areas and preset risk coefficients of the different risk points;
And determining the cross risk coefficient of the monitoring device by utilizing the sub-region risk coefficients of different risk sub-regions, and determining the construction risk factor of the monitoring device by the basic risk coefficient and the cross risk coefficient of the monitoring device.
Further, the cross risk coefficient of the monitoring device is determined according to the sum of sub-region risk coefficients of different risk sub-regions.
In addition, it should be noted that the value range of the construction risk factor of the monitoring device is between 0 and 1, where when the construction risk factor of the monitoring device is greater than a preset coefficient threshold, it is determined that the monitoring device corresponding to the monitoring device needs to be set by an analysis device separately.
It is further understood that the method for determining the construction risk factor of the monitoring device is as follows:
Determining the number and distribution positions of different types of risk points of the monitoring device based on the distribution data of the risk points;
Determining the interval distance between different risk points and other risk points based on the distribution positions of the risk points, determining other risk points with cross construction risks with the risk points through the interval distance, taking the other risk points as cross construction risk points, and determining risk evaluation coefficients of different risk points based on the types of the different risk points and the number of the cross construction risk points;
and determining construction risk factors of the monitoring positions through risk assessment coefficients of different risk points.
It is further understood that the method for determining the construction risk factor of the monitoring location is as follows:
S11, determining the number of different types of risk points of the monitoring position based on the distribution data of the risk points, judging whether the number of the risk points of the monitoring position is larger than the preset number of the risk points, if so, entering the next step, and if not, entering the step S14;
S12, determining preset risk coefficients of different risk points by using the types of the risk points, judging whether the average value of the preset risk coefficients of the different risk points is larger than a preset risk coefficient threshold value, if so, determining that a monitoring device corresponding to the monitoring device needs to be independently set by an analysis device, and if not, entering the next step;
S13, determining risk points with preset risk coefficients larger than the maximum value of the set risk coefficients according to preset risk coefficients of different risk points, judging whether the number of the risk points with the preset risk coefficients larger than the maximum value of the set risk coefficients meets the requirement, if so, entering the next step, and if not, determining that the monitoring device corresponding to the monitoring device needs to be independently set by an analysis device;
S14, determining a basic risk coefficient of the monitoring device according to the number of different types of risk points of the monitoring device and preset risk coefficients of different types of risk points, judging whether the basic risk coefficient of the monitoring device meets requirements, if so, entering a next step, and if not, determining that the monitoring device corresponding to the monitoring position needs to be independently set by an analysis device;
S15, determining distribution positions of different types of risk points in the monitoring area based on distribution data of different types of risk points, determining risk subareas with cross construction risks through the distribution positions, determining subarea risk coefficients of the risk subareas according to the number of the risk points in the different risk subareas and preset risk coefficients of the different risk points, judging whether the number of the risk subareas which are not satisfied with requirements is satisfied with the subarea risk coefficients, if yes, entering a next step, and if no, determining that a monitoring device corresponding to the monitoring position needs to be independently set by an analysis device;
s16, determining a cross risk coefficient of the monitoring device by utilizing sub-region risk coefficients of different risk sub-regions, and determining a construction risk factor of the monitoring device through a basic risk coefficient and the cross risk coefficient of the monitoring device.
S2, dividing the general devices into different monitoring device combinations by using the monitoring positions, and determining the set number of analysis devices of the monitoring device combinations based on the distances between the general devices in the monitoring device combinations and construction risk factors of the different general devices;
further, the general device is divided into different monitoring device combinations, which specifically includes:
Dividing a building construction site into a plurality of areas by utilizing a preset area, and determining areas corresponding to different general devices based on the areas where the monitoring positions of the general devices are located;
The general devices in the same area are divided into the same monitoring device combination.
As shown in fig. 3, the method for determining the number of the analysis devices in the monitoring device combination includes:
Determining average distance between adjacent general devices by utilizing the distance between the general devices in the monitoring device combination, and determining distribution discrete coefficients of the monitoring device combination according to preset discrete coefficients corresponding to the average distance;
Determining the average value of construction risk factors of different general devices in the monitoring device combination by using the construction risk factors of the different general devices in the monitoring device combination, and taking the average value as a combined risk factor;
the set number of analysis devices in the monitoring device combination is determined based on the number of monitoring device combinations, the combination risk coefficient, and the distribution discrete coefficient.
Further, determining the set number of analysis devices in the monitoring device combination based on the number of the monitoring device combination, the combination risk coefficient and the distribution discrete coefficient specifically includes:
Determining a minimum required number of analysis devices in the monitoring device combination based on the number of the monitoring device combination;
Determining a preset scale factor of the required quantity under the combined risk coefficient and a preset scale factor of the required quantity under the distributed discrete coefficient based on the combined risk coefficient and the distributed discrete coefficient;
and taking the product of the preset scaling factor of the required quantity, the preset scaling factor of the required quantity under the distributed discrete coefficient and the minimum required quantity as the set quantity of the analysis devices in the monitoring device combination.
It is further understood that the method for determining the number of settings of the analysis device in the monitoring device combination is:
Determining average distance between adjacent general devices by utilizing the distance between the general devices in the monitoring device combination, determining distribution discrete coefficients of the monitoring device combination according to preset discrete coefficients corresponding to the average distance, and determining the set required quantity of analysis devices under the distribution discrete coefficients by adopting a preset mapping function according to the quantity of the distribution discrete coefficients and the monitoring device combination;
Determining the average value of construction risk factors of different general devices in the monitoring device combination through the construction risk factors of the different general devices in the monitoring device combination, taking the average value as a combined risk factor, and determining the set required quantity of analysis devices under the combined risk factor through the combined risk factor and the number of the monitoring device combination by adopting a preset mapping function;
the number of the analysis devices in the monitoring device combination is determined based on the number of the set requirements of the analysis devices under the distribution discrete coefficient and the number of the set requirements of the analysis devices under the combination risk coefficient.
Further, determining the set number of analysis devices in the monitoring device combination based on the set demand number of analysis devices under the distributed discrete coefficient and the set demand number of analysis devices under the combined risk coefficient specifically includes:
And taking the maximum value of the set required quantity of the analysis devices under the distribution discrete coefficient and the set required quantity of the analysis devices under the combined risk coefficient as the set quantity of the analysis devices in the monitoring device combination.
In addition, the method for determining the set number of the analysis devices in the monitoring device combination is as follows:
S21, acquiring the number of general devices in the monitoring device combination, judging whether the number of the general devices is smaller than the number of preset devices, if so, determining the set number of analysis devices in the monitoring device combination by using the preset number, and if not, entering the next step;
s22, judging whether the number of the general devices is larger than the device limiting number, if so, entering the next step, and if not, entering the step S25;
S23, determining average distance between adjacent general devices by utilizing the distance between the general devices in the monitoring device combination, determining a distribution discrete coefficient of the monitoring device combination according to a preset discrete coefficient corresponding to the average distance, judging whether the distribution discrete coefficient of the monitoring device combination meets the requirement, if so, entering the next step, and if not, determining the set number of analysis devices in the monitoring device combination according to the preset upper limit of the number;
S24, determining the average value of construction risk factors of different general devices in the monitoring device combination by using the construction risk factors of the different general devices in the monitoring device combination, taking the average value as a combination risk factor, judging whether the combination risk factor of the monitoring device combination meets the requirement, if so, entering the next step, and if not, determining the set number of analysis devices in the monitoring device combination according to the preset upper limit of the number;
S25, determining the set number of analysis devices in the monitoring device combination based on the number of the monitoring device combinations, the combination risk coefficient and the distribution discrete coefficient.
S3, taking the monitoring positions and the set number of the general devices as constraint conditions, generating a set scheme of an analysis device of the monitoring device combination, and determining a preferable set scheme in the scheme based on the distance between the analysis devices;
Further, generating a setting scheme of the analysis device in the monitoring device combination specifically includes:
generating a configuration scheme of an analysis device of a monitoring device combination by taking the monitoring positions and the configuration number of general devices as constraint conditions
Taking the monitoring position of the general device as an alternative setting position;
and determining the setting scheme of the analysis device in the monitoring device combination based on the alternative setting positions and the setting number.
Specifically, as shown in fig. 4, the method of the preferred setting scheme in the scheme is as follows:
determining the distance between different analysis devices according to the setting positions of the different analysis devices in the scheme;
Determining set discrete coefficients for different analysis devices using distances of the different analysis devices from other analysis devices in the monitoring device combination;
The method comprises the steps of determining comprehensive setting discrete coefficients of analysis devices in the scheme based on average values of the setting discrete coefficients of different analysis devices, and determining a preferable setting scheme in the scheme based on the comprehensive setting discrete coefficients.
Further, the optimal setting scheme in the optimal setting schemes is a priority setting scheme for comprehensively setting the discrete coefficients to meet the requirements.
In addition, the method of the preferable setting scheme in the scheme is as follows:
s31, determining the distance between different analysis devices according to the setting positions of the different analysis devices in the scheme, judging whether the analysis devices with the distance smaller than a preset distance threshold value exist between the analysis devices and other analysis devices, if so, entering the next step, and if not, determining the scheme as a priority setting scheme;
s32, taking the analysis devices with the distance smaller than a preset distance threshold value with other analysis devices as aggregation setting analysis devices, judging whether the number of the aggregation setting analysis devices is larger than the number of the preset analysis devices, if so, entering the next step, and if not, entering the step S35;
s33, judging whether the quantity ratio of the analysis devices of the aggregation setting analysis device in the monitoring device combination meets the requirement, if so, entering the next step, and if not, determining that the scheme does not belong to a priority setting scheme;
S34, acquiring the number of the aggregation setting analysis devices and the number proportion of the analysis devices in the monitoring device combination, determining the setting aggregation coefficient of the aggregation setting analysis devices by combining the distances between different aggregation setting analysis devices and other analysis devices, judging whether the setting aggregation coefficient of the aggregation setting analysis devices meets the requirement, if so, entering the next step, and if not, determining that the scheme does not belong to the priority setting scheme;
S35, determining set discrete coefficients of different analysis devices by utilizing the distances between the different analysis devices and other analysis devices in the monitoring device combination, judging whether the number of analysis devices with the set discrete coefficients not meeting the requirements is larger than the preset device number, if not, entering the next step, and if so, determining that the scheme does not belong to the priority setting scheme;
s36 determines a comprehensive set discrete coefficient of the analysis devices in the scheme based on an average value of the set discrete coefficients of the different analysis devices, and performs determination of a preferred set scheme in the scheme based on the comprehensive set discrete coefficient.
S4, determining an optimal setting scheme in the optimal setting schemes according to construction risk factors of different general devices in the monitoring device combination and the distance between the construction risk factors and the analysis device, and carrying out local identification processing of the construction risk of the monitoring device combination by utilizing the optimal setting scheme.
Specifically, the method for determining the optimal setting scheme in the optimal setting schemes comprises the following steps:
Determining the risk identification reliability of different general devices based on the distance between the different general devices and the different analysis devices;
determining weight coefficients of risk identification reliability of different general devices by using construction risk factors of different general devices;
and determining the identification reliability of the priority setting scheme by the weight coefficient and the risk identification reliability of different general devices, and determining the optimal setting scheme in the optimal setting scheme according to the identification reliability.
Further, the optimal setting scheme is a preferred setting scheme with the greatest identification reliability.
It can be understood that the local identification processing of the construction risk of the monitoring device combination specifically includes:
Local identification processing of construction safety risks of the monitoring device combination by utilizing the optimal setting scheme
And setting the analysis device in the monitoring device combination by using the optimal setting scheme, carrying out monitoring data of the monitoring devices in the monitoring device combination according to the analysis device in the monitoring device combination, and carrying out safety risk identification processing according to a preset artificial intelligent model.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (7)
1. The utility model provides a calculation and communication device based on artificial intelligence hardware cooperation, has communication module, calculation module, is applied to building site risk identification method, and its characterized in that specifically includes:
Determining construction risk factors of different monitoring devices based on distribution data of risk points of a monitoring area of the monitoring device, taking a computing and communication device as an analysis device, and taking the monitoring device as a general device when the monitoring device is determined not to be required to be independently set by the analysis device based on the construction risk factors;
dividing the general devices into different monitoring device combinations by using the monitoring positions, and determining the set number of analysis devices of the monitoring device combinations based on the distance between the general devices in the monitoring device combinations and the construction risk factors of the different general devices;
generating a setting scheme of analysis devices of the monitoring device combination by taking the monitoring positions and the setting number of the general devices as constraint conditions, and determining a preferable setting scheme in the scheme based on the distance between the analysis devices;
Determining an optimal setting scheme in the optimal setting schemes according to construction risk factors of different general devices in the monitoring device combination and the distance between the construction risk factors and the analysis device, and carrying out local identification processing of the construction risk of the monitoring device combination by utilizing the optimal setting scheme;
the method for determining the set number of the analysis devices in the monitoring device combination comprises the following steps:
Determining average distance between adjacent general devices by utilizing the distance between the general devices in the monitoring device combination, and determining distribution discrete coefficients of the monitoring device combination according to preset discrete coefficients corresponding to the average distance;
Determining the average value of construction risk factors of different general devices in the monitoring device combination by using the construction risk factors of the different general devices in the monitoring device combination, and taking the average value as a combined risk factor;
Determining a set number of analysis devices in the monitoring device combination based on the number of the monitoring device combinations, the combination risk coefficient, and the distribution discrete coefficient;
determining the set number of analysis devices in the monitoring device combination based on the number of the monitoring device combination, the combination risk coefficient and the distribution discrete coefficient specifically comprises the following steps:
Determining a minimum required number of analysis devices in the monitoring device combination based on the number of the monitoring device combination;
Determining a preset scale factor of the required quantity under the combined risk coefficient and a preset scale factor of the required quantity under the distributed discrete coefficient based on the combined risk coefficient and the distributed discrete coefficient;
Taking the product of the preset scale factor of the required quantity, the preset scale factor of the required quantity under the distributed discrete coefficient and the minimum required quantity as the set quantity of the analysis devices in the monitoring device combination;
The method for determining the optimal setting scheme in the optimal setting schemes comprises the following steps:
Determining the risk identification reliability of different general devices based on the distance between the different general devices and the different analysis devices;
determining weight coefficients of risk identification reliability of different general devices by using construction risk factors of different general devices;
And determining the identification reliability of the preferred setting schemes according to the weight coefficient and the risk identification reliability of different general devices, and determining the optimal setting scheme in the preferred setting schemes according to the identification reliability.
2. The computing and communication device based on artificial intelligence hardware cooperation according to claim 1, wherein the communication module is responsible for communication processing with monitoring devices, other analysis devices and unified security monitoring platform.
3. The computing and communication device based on cooperation of artificial intelligence hardware according to claim 1, wherein the computing module is responsible for performing construction safety risk identification processing of monitoring data of the monitoring device by using a preset artificial intelligence model.
4. The computing and communication device based on artificial intelligence hardware cooperation according to claim 1, wherein the method for determining the construction risk factor of the monitoring device comprises the following steps:
Based on the distribution data of the risk points, determining the number of different types of risk points of the monitoring device, and determining a basic risk coefficient of the monitoring device by combining preset risk coefficients of different types of risk points;
determining distribution positions of different types of risk points in the monitoring area based on distribution data of different types of risk points, determining risk sub-areas with cross construction risks through the distribution positions, and determining sub-area risk coefficients of the risk sub-areas according to the number of the risk points in the different risk sub-areas and preset risk coefficients of the different risk points;
And determining the cross risk coefficient of the monitoring device by utilizing the sub-region risk coefficients of different risk sub-regions, and determining the construction risk factor of the monitoring device by the basic risk coefficient and the cross risk coefficient of the monitoring device.
5. The artificial intelligence hardware-collaboration-based computing and communication device of claim 4, wherein the cross risk coefficient of the monitoring device is determined based on a sum of sub-region risk coefficients of different risk sub-regions.
6. The computing and communication device based on artificial intelligence hardware cooperation according to claim 1, wherein the generic device is divided into different combinations of monitoring devices, in particular comprising:
Dividing a building construction site into a plurality of areas by utilizing a preset area, and determining areas corresponding to different general devices based on the areas where the monitoring positions of the general devices are located;
The general devices in the same area are divided into the same monitoring device combination.
7. The artificial intelligence hardware-collaboration-based computing and communication device of claim 1, wherein the optimal setting is a preferred setting that identifies a greatest reliability.
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