Construction environment intelligent monitoring system based on Internet of Things

Abstract

The invention belongs to the field of construction environment monitoring, relates to a data analysis technology, and aims to solve the problem that corresponding environment monitoring alarm modes cannot be configured for different construction stages in combination with historical data in the prior art, in particular to a construction environment intelligent monitoring system based on the Internet of Things. Comprising a data statistics module, an optimization evaluation module and a screening management module which are connected in sequence, and the data statistics module, the optimization evaluation module and the screening management module are all in communication connection with a database; the data statistics module is used for performing statistical analysis on the construction environment monitoring data: dividing a construction project into a plurality of construction stages, generating a statistical period, and obtaining accident data of the construction stages corresponding to all the construction projects at the end moment of the statistical period; according to the method, dynamic optimization of a construction environment monitoring strategy is realized, the problem of contradiction between monitoring sensitivity and accuracy caused by a fixed threshold system is solved, and the system can automatically identify key risk parameters of each construction link through staged data analysis.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of construction environment monitoring, and relates to data analysis technology, in particular to a construction environment intelligent monitoring system based on the Internet of Things. BACKGROUND

[0002] The construction environment intelligent monitoring system based on the Internet of Things is an intelligent solution for real-time monitoring of construction site environmental parameters using Internet of Things technology. The system realizes comprehensive monitoring, early warning and management of construction site environmental quality by deploying various types of environmental sensor networks, combining wireless communication technology and cloud computing platforms.

[0003] The invention patent with the publication number CN118333375B discloses a construction safety monitoring method and device based on construction environment. The monitoring method is based on outdoor temperature and outdoor wind speed of the construction environment, combined with construction duration to evaluate construction risks, and timely issues a reminder information when determining the existence of construction risks, effectively protecting the personal safety of construction personnel and providing protection for outdoor construction safety. However, the environmental risks of construction projects are not the same in different construction stages, and the existing technology cannot equip corresponding environmental monitoring alarm methods for different construction stages combined with historical data, nor can it extract and mark key environmental parameters in different construction stages, resulting in ineffective protection of construction safety.

[0004] In view of the above technical problems, the present application provides a solution. SUMMARY

[0005] The purpose of the present application is to provide a construction environment intelligent monitoring system based on the Internet of Things, which can solve the problem that the existing technology cannot equip corresponding environmental monitoring alarm methods for different construction stages combined with historical data.

[0006] The technical problem to be solved by the present application is how to provide a construction environment intelligent monitoring system based on the Internet of Things that can equip corresponding environmental monitoring alarm methods for different construction stages combined with historical data.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] The construction environment intelligent monitoring system based on the Internet of Things comprises a data statistics module, an optimization evaluation module and a screening management module connected in sequence, and the data statistics module, the optimization evaluation module and the screening management module are in communication connection with a database.

[0009] The data statistics module is used for statistical analysis of the construction environment monitoring data: the construction projects are divided into several construction stages, a statistical period is generated, accident data of all construction projects corresponding to the construction stages are acquired at the end of the statistical period, the number of accidents of the construction projects in the same construction stage within the statistical period is marked as an accident value, an environment monitoring parameter of the construction site is marked as a monitoring object i, i = 1, 2, …, n, and an analysis optimization value FYi of the monitoring object i in the construction stage is marked;

[0010] The optimization evaluation module is used for optimization analysis of the construction environment monitoring process: the over-limit alarm threshold CBi of the monitoring object i in the construction stage is called, the analysis optimization value FYi of the monitoring object i is compared with the over-limit alarm threshold CBi, and whether the monitoring object i has a monitoring optimization feature in the corresponding construction stage is determined according to the comparison result;

[0011] The screening management module is used for screening analysis of the key monitoring direction of the construction environment.

[0012] Further, the specific process of marking the analysis optimization value FYi of the monitoring object i in the construction stage includes: the monitoring values JCi of the same monitoring object i corresponding to the same construction stage constitute a statistical set i of the monitoring object i, the statistical set i is cleaned to obtain the analysis optimization value FYi, and the analysis optimization values FYi of the monitoring objects i of all construction stages are sent to the optimization evaluation module.

[0013] Further, the specific process of cleaning the statistical set i includes: variance calculation is performed on all elements of the statistical set i to obtain an analysis coefficient FXi of the statistical set i, the analysis coefficient FXi is compared with a preset analysis threshold FXd, if the analysis coefficient FXi is greater than the analysis threshold FXd, the maximum element and the minimum element in the statistical set i are removed, then the analysis coefficient FXi is recalculated, and the process is repeated until the analysis coefficient FXi is less than or equal to the analysis threshold FXd, and if the analysis coefficient FXi is less than the analysis threshold FXd, the minimum element in the statistical set i is marked as the analysis optimization value FYi of the monitoring object i.

[0014] Further, the specific process of comparing the analysis optimization value FYi of the monitoring object i with the over-limit alarm threshold CBi includes: if the analysis optimization value FYi is less than the over-limit alarm threshold CBi, it is determined that the monitoring object i has the monitoring optimization feature in the corresponding construction stage, and in the next statistical period, the value of the analysis optimization value FYi is replaced by the value of the over-limit alarm threshold CBi; if the analysis optimization value FYi is greater than or equal to the over-limit alarm threshold CBi, it is determined that the monitoring object i does not have the monitoring optimization feature in the corresponding construction stage, the corresponding monitoring object i is marked as a trigger object e, e = 1, 2, …, m, m is a positive integer, and m ≤ n, a risk treatment optimization signal is generated and sent to the mobile terminal of the corresponding construction team manager.

[0015] Further, the specific process of screening and analyzing the key monitoring direction of the construction environment by the screening management module includes: marking the key parameters, and when the key parameters exceed the corresponding over-limit alarm threshold in the next statistical period, generating a stop work treatment signal and sending the stop work treatment signal to the mobile terminal of the manager; marking the L3 construction stages with the largest accident value as key stages, and optimizing the over-limit alarm threshold CBi of the monitoring object i in the key stages.

[0016] Further, the specific process of marking the key parameters includes: marking the accident process in the construction stage with the number of trigger objects e less than L1 as a key process, marking the trigger object e corresponding to the key process as a key object, marking the number of times that the monitoring object i is marked as a key object as the key value ZDi of the monitoring object i, and marking the L2 monitoring objects i with the largest key value ZDi as key parameters.

[0017] Further, the specific process of optimizing the over-limit alarm threshold CBi of the monitoring object i in the key stages includes: calling the over-limit alarm threshold CBi of the monitoring object i in the key stages, obtaining the optimization threshold YYi of the monitoring object i through the formula YYi = t1 × CBi, wherein t1 is a proportional coefficient, 0.85 ≤ t1 ≤ 0.95, and replacing the value of the optimization threshold YYi with the value of the over-limit alarm threshold CBi.

[0018] Further, the working method of the construction environment intelligent monitoring system based on the Internet of Things includes the following steps:

[0019] Step one: statistical analysis of construction environment monitoring data: divide the construction project into several construction stages, generate a statistical period, and obtain the analysis optimization value FYi of the monitoring object i corresponding to the construction stage of all construction projects at the end of the statistical period;

[0020] Step two: optimize the analysis of the construction environment monitoring process: compare the analysis optimization value FYi of the monitoring object i with the over-standard alarm threshold CBi, and determine whether the monitoring object i has a monitoring optimization feature in the corresponding construction stage through the comparison result;

[0021] Step three: screening analysis of the key monitoring direction of the construction environment: mark the key parameters and key stages, and monitor the construction environment according to the key parameters and key stages.

[0022] The present application has the following beneficial effects:

[0023] 1. The present application realizes the dynamic optimization of the construction environment monitoring strategy, solves the contradiction between monitoring sensitivity and accuracy caused by the fixed threshold system, and through phased data analysis, the system can automatically identify the key risk parameters of each construction link, for example, in the steel structure hoisting stage, the wind speed change is monitored, and in the waterproof construction stage, the flammable gas detection is strengthened. This targeted monitoring mechanism not only improves the monitoring intensity of high-risk links, but also reduces the resource consumption of low-risk links, effectively improving the fine level of construction safety management;

[0024] 2. The present application effectively solves the misjudgment problem caused by the fluctuation of monitoring data in the construction stage, and through the variance iteration cleaning mechanism, the abnormal data generated by extreme weather, equipment failure and other occasional factors are excluded, so that the analysis optimization value can stably reflect the real environment state of the construction stage. The processing process provides data quality guarantee for the dynamic optimization of the over-standard alarm threshold, thereby improving the identification accuracy of the monitoring system for the construction environment risk;

[0025] 3. The present application can dynamically optimize the alarm threshold according to the actual monitoring data of the construction stage, avoid the monitoring failure problem caused by unreasonable fixed threshold setting, and at the same time, through the triggering object marking and alarm signal directional pushing, the risk response time is shortened, and the real-time and accuracy of the construction site environment monitoring are improved;

[0026] 4. The present application solves the problem that the monitoring alarm mode in the prior art does not match the construction stage risk, can automatically identify high-risk parameters and stages, and through threshold optimization, early warning is realized. For example, after reducing the alarm threshold in the key stage, the system can trigger the alarm in advance when the environmental parameter approaches the danger level, avoiding accidents; at the same time, the marking of the key parameters enables the management personnel to intensively strengthen the resource allocation of the specific monitoring direction, thereby improving the construction safety guarantee efficiency;

[0027] 5、The application can automatically identify the environmental monitoring parameters highly related to safety accidents in the construction process, accurately screen out the monitoring indicators that need to be focused on by quantitatively analyzing the influence frequency of each parameter in historical accidents. This effectively solves the problem of fixed monitoring direction in traditional methods, cannot dynamically optimize, makes the construction environmental monitoring resources be reasonably distributed, improves the identification accuracy and response speed of high-risk environmental factors, for example, in the concrete pouring stage, the system can automatically strengthen the temperature and humidity monitoring frequency in this stage by analyzing the key values, which significantly improves the construction safety control effect. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0029] Figure 1 The system block diagram of the first embodiment of the present application is shown in the figure.

[0030] Figure 2 The method flow chart of the second embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0031] The technical solutions of the present application will be described in detail below in combination with embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0032] In the prior art, the construction environmental monitoring system generally uses fixed threshold value for alarm judgment, and does not consider the differentiating characteristics of environmental risks in different construction stages. For example, some monitoring methods only trigger early warning according to the comparison between real-time environmental parameters and preset threshold value, but do not establish a correlation analysis mechanism between construction stages and historical data. When encountering large fluctuation of dust concentration in earth excavation stage or continuous over-standard noise in main construction stage, the fixed threshold system is easy to produce false alarm or miss alarm, which cannot dynamically optimize the monitoring strategy, and is also difficult to identify key risk parameters, resulting in unreasonable allocation of monitoring resources.

[0033] To address the aforementioned issues, the inventors discovered a strong correlation between construction safety accidents and abnormal environmental parameters at specific construction stages. Analysis of a subway construction project revealed that gas concentration exceedances occurred during the foundation pit support stage, accounting for 63% of all accidents, while dust exceedance alarms were less than 40% effective during the finishing stage. This suggests the need for a phased monitoring mechanism: first, the construction process should be divided into several stages with clearly defined risk characteristics, and historical data should be used to determine the optimization direction for key parameters at each stage; second, alarm thresholds should be dynamically adjusted based on stage characteristics to avoid monitoring failures caused by using uniform standards; finally, high-frequency, high-risk parameters should be selected for focused monitoring, forming a dynamically optimized monitoring system.

[0034] Example 1: As Figure 1 As shown, the IoT-based intelligent monitoring system for construction environment includes a data statistics module, an optimization evaluation module, and a screening management module connected in sequence. The data statistics module, optimization evaluation module, and screening management module of the obstacle recognition-based adaptive asynchronous control system for railway freight trains are all connected to the database.

[0035] The data statistics module of the obstacle recognition-based adaptive asynchronous control system for railway freight trains is used to perform statistical analysis on construction environment monitoring data. The construction project is divided into several construction stages, generating a statistical period. At the end of the statistical period, accident data for all construction stages corresponding to the current construction stage are acquired. The number of accidents in the same construction stage within the statistical period is marked as the accident value. The environmental monitoring parameters at the construction site are marked as monitoring objects i, i = 1, 2, ..., n. The monitoring value of monitoring object i during the accident process corresponding to the construction stage is marked as the monitoring value JCi. The monitoring values ​​JCi corresponding to the same monitoring object i in the same construction stage constitute the statistical set i of monitoring object i. The statistical set i is cleaned by calculating the variance of all elements in the statistical set i to obtain the analysis coefficient FXi. The analysis coefficient FXi is then compared with the preset analysis threshold FXd. If the analysis coefficient FXi is greater than the analysis threshold FXd, the largest and smallest elements in the statistical set i are removed, and the analysis coefficient FXi is recalculated. This process is repeated until the analysis coefficient FXi is less than or equal to the analysis threshold FXd. If the analysis coefficient FXi is less than the analysis threshold FXd, the smallest element in the statistical set i is marked as the analysis optimization value FYi of the monitoring object i. The analysis optimization values ​​FYi of all monitoring objects i in the construction stage are sent to the optimization evaluation module.

[0036] The statistical set i refers to a set formed by all monitoring values of the same monitoring object i in the same construction stage. The time series data table in the database can be used for storage, and the data can be classified by time stamp and construction stage label. The cleaning process refers to the process of removing outliers from the statistical set i. The iterative algorithm of variance calculation combined with threshold comparison can be used to remove extreme values with excessive dispersion to improve data reliability. The analysis optimization value FYi refers to the representative monitoring value determined after cleaning. The minimum value in the statistical set i that meets the variance constraint can be used to reflect the baseline state of the monitoring object in the construction stage.

[0037] Specifically, the monitoring value JCi is continuously collected during the construction period and stored in groups according to the construction stage label to form the statistical set i. During the cleaning process, the variance of the statistical set i is first calculated as the analysis coefficient FXi. If the coefficient exceeds the preset threshold FXd, the maximum and minimum values are removed by iteration until the variance meets the requirements. The minimum monitoring value in the final retained statistical set i is marked as the analysis optimization value FYi, which is transmitted to the optimization evaluation module as the basic parameter reflecting the environmental state of the construction stage for threshold comparison.

[0038] Specifically, at the end of the statistical period, all monitoring values of a monitoring object in the same construction stage are integrated into a statistical set i. The analysis coefficient FXi is obtained by calculating the variance of the set. When the analysis coefficient FXi exceeds the preset threshold, it indicates that the data fluctuation is large and there may be abnormal value interference. At this time, the system automatically removes the maximum and minimum elements and recalculates the variance, and the process is repeated until the variance meets the threshold requirement. The minimum element in the final retained statistical set is marked as the analysis optimization value, which reflects the stable monitoring baseline of the monitoring object in the construction stage. For example, in the concrete pouring stage, if the dust concentration monitoring data is abnormally high due to equipment failure, the analysis optimization value obtained by removing the extreme value multiple times can effectively exclude the interference and accurately reflect the actual environmental state.

[0039] The railway freight train adaptive asynchronous control system optimization evaluation module based on obstacle identification is used for optimizing analysis on the construction environment monitoring process: the exceeding alarm threshold CBi of the monitoring object i in the construction stage is called, and the analysis optimization value FYi of the monitoring object i is compared with the exceeding alarm threshold CBi: if the analysis optimization value FYi is less than the exceeding alarm threshold CBi, it is determined that the monitoring object i has a monitoring optimization feature in the corresponding construction stage, and in the next statistical period, the value of the analysis optimization value FYi is replaced with the value of the exceeding alarm threshold CBi when the environment is monitored; if the analysis optimization value FYi is greater than or equal to the exceeding alarm threshold CBi, it is determined that the monitoring object i does not have a monitoring optimization feature in the corresponding construction stage, the corresponding monitoring object i is marked as a trigger object e, e=1, 2, …, m, m is a positive integer, and m≤n, a risk treatment optimization signal is generated and sent to the mobile terminal of the corresponding construction team manager.

[0040] Among them, the analysis optimization value FYi refers to the representative value of the monitoring parameter obtained after data cleaning processing, which can be realized by using the minimum monitoring value after removing abnormal values by variance iteration, which reflects the lower limit of the safety fluctuation of the monitoring object in the construction stage. The exceeding alarm threshold CBi refers to the preset environmental parameter safety standard, which can be realized by using the critical value set by the industry specification or historical accident data, which is used to judge whether the monitoring object is in a dangerous state. The trigger object e refers to the monitoring parameter entity that exceeds the safety standard, which can be realized by data labeling processing, which is used to mark the monitoring object that needs to be paid attention to. The risk treatment optimization signal refers to the digital instruction of triggering alarm, which can be realized by encapsulating alarm information using mobile communication protocol to realize real-time pushing to the terminal of the manager.

[0041] Specifically, after completing the monitoring data cleaning, the analysis optimization value is compared with the preset alarm threshold. When the analysis optimization value is lower than the alarm threshold, it indicates that the safety fluctuation range of the monitoring object in the statistical period does not touch the dangerous boundary, at this time the alarm threshold is dynamically updated as the analysis optimization value, thereby reducing the redundant alarm frequency of subsequent monitoring. When the analysis optimization value reaches or exceeds the alarm threshold, it indicates that the monitoring object has potential risks, and the system automatically classifies it as a trigger object and generates an alarm signal, and pushes the specific parameter information to the manager through the mobile communication network, prompting the site to take targeted control measures.

[0042] The screening management module of the railway freight train adaptive asynchronous control system based on obstacle identification is used for screening analysis on the key monitoring direction of the construction environment: marking an accident process in which the number of triggered objects e is less than L1 in the construction stage as a key process, marking the triggered object e corresponding to the key process as a key object, marking the number of times that the monitoring object i is marked as a key object as the key value ZDi of the monitoring object i, marking the L2 monitoring objects i with the largest key value ZDi as key parameters, and generating a shutdown processing signal and sending the shutdown processing signal to the mobile terminal of the management personnel when the key parameters exceed the corresponding threshold value in the next statistical period; marking the L3 construction stages with the largest accident value as key stages, calling the exceeding alarm threshold value CBi of the monitoring object i in the key stage, obtaining the optimization threshold value YYi of the monitoring object i through the formula YYi=t1*CBi, wherein t1 is a proportional coefficient, 0.85≤t1≤0.95, and replacing the value of the optimization threshold value YYi with the value of the exceeding alarm threshold value CBi.

[0043] Among them, the key parameter refers to an environmental monitoring parameter that needs to be paid attention to first, which can be marked by counting the number and frequency of triggered objects e, for example, the monitoring object i with a trigger frequency exceeding a set threshold value is defined as a key parameter, thereby realizing dynamic identification of high-risk parameters.

[0044] Among them, the shutdown processing signal refers to a control instruction triggering a specific operation, which can be generated through a preset logic condition, for example, when the key parameter exceeds the alarm threshold value in the statistical period, the signal is automatically triggered and pushed to the management personnel terminal, thereby realizing rapid response to abnormal state.

[0045] Among them, the accident value refers to a statistical index of the number of accidents in a construction stage, which can be calculated by accumulating the accident data of all construction projects in the same construction stage, for example, the number of accidents is taken as the quantitative basis, thereby identifying high-risk construction stages.

[0046] Among them, the key stage refers to a construction stage with a high incidence of accidents, which can be screened by sorting the accident values, for example, the construction stages ranked in the top L3 in terms of accident values are marked as key stages, and L3 can be an integer between 3 and 5, thereby realizing phased focusing of construction risks.

[0047] Among them, the exceeding alarm threshold value optimization refers to adjusting the alarm condition of the monitoring parameter, which can be dynamically corrected by a proportional coefficient, for example, the original threshold value is multiplied by a coefficient between 0.85 and 0.95 to generate an optimized threshold value, thereby reducing the alarm triggering condition of the key stage to improve the monitoring sensitivity.

[0048] Specifically, the screening and management module identifies key parameters and key phases by analyzing historical data. Key parameter marking is based on the statistical analysis of the number of triggering objects (e). For example, if a monitored object frequently triggers alarms across multiple construction phases, it is marked as a key parameter. In the next statistical period, if this parameter exceeds a threshold, the system immediately generates a stop-work signal and notifies management personnel. Simultaneously, the three construction phases with the highest accident values ​​(L3) are marked as key phases. For example, for the top three construction phases with the highest accident values, the system automatically retrieves the exceeding alarm threshold for their monitored objects, generates an optimized threshold using a proportional coefficient t1 (e.g., 0.9), and replaces the original threshold to enhance monitoring.

[0049] This application addresses the mismatch between existing monitoring and alarm methods and the risks at different construction stages. It can automatically identify high-risk parameters and stages and provide early warnings through threshold optimization. For example, by lowering the alarm threshold during key stages, the system can trigger alarms in advance when environmental parameters approach dangerous levels, preventing accidents. Simultaneously, the marking of key parameters allows managers to strategically allocate resources for specific monitoring areas, improving the efficiency of construction safety assurance.

[0050] Specifically, after the statistical period ends, the system automatically selects accident processes where the number of triggering objects does not reach L1 as key processes. For example, when L1 is set to 3, a construction phase can be marked as a key process if only 2 triggering objects occur within the statistical period. For each key process, the system marks the triggering objects that are determined to have monitoring optimization characteristics as key objects. For example, if dust concentration and noise decibels exceed the standard in a key process, these two parameters are marked as key objects. The system continuously counts the number of times each monitoring object is marked as a key object to form a key value. For example, if the temperature parameter of a monitoring point is marked as a key object in three statistical periods, its key value accumulates to 3. Finally, the top L2 monitoring objects are selected as key parameters in descending order of key value. For example, when L2 is set to 5, the system automatically selects the 5 parameters with the highest key values ​​as the key monitoring objects for the next stage.

[0051] Example 2: Figure 2 As shown, the IoT-based intelligent monitoring method for the construction environment includes the following steps:

[0052] Step 1: Perform statistical analysis on construction environment monitoring data: Divide the construction project into several construction stages, generate a statistical period, and at the end of the statistical period, obtain the analysis and optimization value FYi of the monitoring object i for all construction projects corresponding to the construction stage;

[0053] Step two: optimization analysis on the construction environment monitoring process: comparing the analysis optimization value FYi of the monitoring object i with the over-limit alarm threshold CBi and determining whether the monitoring object i has the monitoring optimization feature in the corresponding construction stage according to the comparison result;

[0054] Step three: screening analysis on the key monitoring direction of the construction environment: marking the key parameters and key stages and monitoring the construction environment according to the key parameters and key stages.

[0055] The construction environment intelligent monitoring system based on the Internet of Things, when working, divides the construction project into a plurality of construction stages, generates a statistical period, obtains the analysis optimization value FYi of the monitoring object i of the corresponding construction stage of all construction projects at the end of the statistical period, compares the analysis optimization value FYi of the monitoring object i with the over-limit alarm threshold CBi and determines whether the monitoring object i has the monitoring optimization feature in the corresponding construction stage according to the comparison result, marks the key parameters and key stages and monitors the construction environment according to the key parameters and key stages.

[0056] The above content is only an example and description of the structure of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the structure of the application or exceed the scope defined by the claims.

[0057] In the description of the present application, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0058] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present application. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and use the present application. The present application is limited by the claims and their entire scope and equivalents.

Claims

1. An intelligent monitoring system for construction environment based on Internet of Things, characterized in that, Comprise data statistics module, optimization evaluation module and screening management module connected in turn, the data statistics module, optimization evaluation module, screening management module are all with database communication connection; The data statistics module is used for statistical analysis of construction environment monitoring data: the construction project is divided into several construction stages, a statistical cycle is generated, the accident data of all construction projects corresponding to the construction stage is obtained at the end of the statistical cycle, the accident number of the construction project in the same construction stage within the statistical cycle is marked as the accident value, the environmental monitoring parameter of the construction site is marked as the monitoring object i, i=1, 2, …, n, the analysis optimization value FYi of the monitoring object i in the construction stage is marked; The optimization evaluation module is used for optimization analysis of the construction environment monitoring process: the alarm threshold CBi of the monitoring object i in the construction stage is called, the analysis optimization value FYi of the monitoring object i is compared with the alarm threshold CBi, and whether the monitoring object i has the monitoring optimization characteristic in the corresponding construction stage is determined through the comparison result; The screening management module is used for screening analysis of the key monitoring direction of the construction environment.

2. The IoT based intelligent monitoring system of construction environment as claimed in claim 1 wherein, The specific process of marking the analysis optimization value FYi of the monitoring object i in the construction stage includes: the monitoring value JCi of the same monitoring object i from the same construction stage constitutes the statistical set i of the monitoring object i, and the analysis optimization value FYi is obtained by cleaning the statistical set i;The analysis optimization value FYi of the monitoring object i of all construction stages is sent to the optimization evaluation module. 3.The IoT-based intelligent monitoring system of construction environment according to claim 2, characterized in that, The specific process of cleaning the statistical set i includes: the analysis coefficient FXi of the statistical set i is obtained by calculating the variance of all elements of the statistical set i, the analysis coefficient FXi is compared with the preset analysis threshold FXd: if the analysis coefficient FXi is greater than the analysis threshold FXd, the maximum element and the minimum element in the statistical set i are removed, and then the analysis coefficient FXi is recalculated, and so on, until the analysis coefficient FXi is less than or equal to the analysis threshold FXd;If the analysis coefficient FXi is less than the analysis threshold FXd, the minimum element in the statistical set i is marked as the analysis optimization value FYi of the monitoring object i.

4. The IoT based intelligent monitoring system of construction environment as claimed in claim 3 wherein, The specific process of comparing the analysis optimization value FYi of the monitoring object i with the alarm threshold CBi includes: if the analysis optimization value FYi is less than the alarm threshold CBi, it is determined that the monitoring object i has the monitoring optimization characteristic in the corresponding construction stage, and in the next statistical cycle, the value of the analysis optimization value FYi is replaced with the value of the alarm threshold CBi;If the analysis optimization value FYi is greater than or equal to the alarm threshold CBi, it is determined that the monitoring object i does not have the monitoring optimization characteristic in the corresponding construction stage, the corresponding monitoring object i is marked as the trigger object e, e=1, 2, …, m, m is a positive integer, and m≤n, a risk processing optimization signal is generated and sent to the mobile terminal of the corresponding construction team manager. 5.The IoT-based intelligent monitoring system of construction environment according to claim 4, wherein, The specific process of screening analysis of the screening management module on the key monitoring direction of the construction environment includes: marking the key parameters, and when the key parameters exceed the corresponding over-limit alarm threshold in the next statistical period, generating a shutdown processing signal and sending the shutdown processing signal to the mobile terminal of the management personnel; marking the L3 construction stages with the largest accident value as key stages, and optimizing the over-limit alarm threshold CBi of the monitoring object i in the key stages. 6.The IoT-based intelligent monitoring system of construction environment according to claim 5, wherein, The specific process of marking the key parameters includes: marking the accident process with the number of trigger objects e in the construction stage less than L1 as a key process, marking the trigger object e corresponding to the key process as a key object, marking the number of times that the monitoring object i is marked as a key object as the key value ZDi of the monitoring object i, and marking the L2 monitoring objects i with the largest key value ZDi as key parameters. 7.The IoT-based intelligent monitoring system of construction environment according to claim 6, wherein, The specific process of optimizing the over-limit alarm threshold CBi of the monitoring object i in the key stages includes: calling the over-limit alarm threshold CBi of the monitoring object i in the key stages, obtaining the optimized threshold YYi of the monitoring object i through the formula YYi=t1xCBi, wherein t1 is a proportional coefficient, 0.85≤t1≤0.95, and replacing the value of the optimized threshold YYi with the value of the over-limit alarm threshold CBi.

8. The IoT based intelligent monitoring system of construction environment as claimed in any one of the claims 1 to 7, wherein, The working method of the construction environment intelligent monitoring system based on the Internet of Things includes the following steps: Step one: statistical analysis of construction environment monitoring data: divide the construction project into several construction stages, generate a statistical period, and obtain the analysis optimization value FYi of the monitoring object i of all construction stages corresponding to the construction project at the end of the statistical period; Step two: optimization analysis of the construction environment monitoring process: compare the analysis optimization value FYi of the monitoring object i with the over-limit alarm threshold CBi and determine whether the monitoring object i has monitoring optimization characteristics in the corresponding construction stage according to the comparison result; Step three: screening analysis of the key monitoring direction of the construction environment: mark the key parameters and key stages, and monitor and optimize the construction environment according to the key parameters and key stages.

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