A Smart Construction Site Safety Control Method and System Based on Sensor Monitoring

Abstract

The present invention discloses a smart construction site safety control method and system based on sensor monitoring, which relates to the technical field of management monitoring. It includes obtaining control index data, the control index data acting on the monitoring devices in each of the detection control areas, obtaining the historical monitoring data in each detection control area, generating a plurality of preset thresholds based on the control index data and the historical monitoring data, and obtaining the real-time monitoring data in each detection control area; generating a plurality of safety warning schemes corresponding to the types of detection control areas based on the real-time monitoring data and the preset thresholds, obtaining the difference data between each safety warning scheme, and adjusting the control authority of the monitoring devices in the detection control area based on the difference data type and value. By combining the real-time monitoring data and the preset thresholds, the present invention can quickly generate targeted safety warning schemes, thereby improving the efficiency and accuracy of construction site safety management.

Description

Technical Field

[0001] The present invention relates to the technical field of management monitoring, and specifically provides an intelligent construction site safety control method and system based on sensor monitoring. Background Technique

[0002] On a construction site, there are not only various large-scale mechanical equipment but also a variety of building materials, so it is a frequent place for safety accidents. For example, there are some dangerous areas on the construction site. If workers do not understand and accidentally enter this area, it is very likely to be dangerous. In addition, for new employees, their safety awareness is not strong and they do not understand various safety wearing devices, so it is very easy to have accidents. Therefore, to fundamentally eliminate potential safety hazards, it is necessary to enhance their safety awareness through repeated training. However, this method not only has a high training cost but also has poor results. Therefore, there has emerged an intelligent construction site safety control method and system based on sensor monitoring on the market.

[0003] After retrieval, the Chinese invention patent with the publication number "CN111045372A" discloses "an intelligent construction site management system". The application analyzes the data of the input unit and the acquisition unit by an analysis unit and uses a preset model to analyze the project progress, so that managers can view device information, personnel information, and environmental information through a viewing unit, and can also view the overall progress of the project through the viewing unit to understand the current progress of the project and facilitate making more reasonable decisions, improving the management efficiency to a certain extent.

[0004] In addition, the Chinese invention patent with the publication number "CN113721564A" discloses an "intelligent workshop safety management system". The application realizes the automatic monitoring, response, recording, analysis and other management functions of safety events such as workshop personnel, equipment, energy, and disasters through a personnel safety management subsystem, an equipment safety management subsystem, a video monitoring subsystem, and a control center, and conducts comprehensive intelligent safety management of the workshop to ensure that while the degree of intelligence of the workshop is improved, safety problems are effectively solved and the production efficiency of the workshop is further improved.

[0005] Since there are many detection and control areas in a large construction site, and the required safety warning schemes and monitoring device control authorities for each detection and control area are not the same. However, when the above-mentioned disclosed patent methods and similar methods are actually operated, they cannot flexibly adapt to the needs of different detection and control areas and make corresponding warning schemes, resulting in low safety management efficiency. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent construction site safety control method and system based on sensor monitoring to solve the problems raised in the above background technique.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] In a first aspect, a smart construction site safety control method based on sensor monitoring is proposed and applied to a smart construction site safety control system. The smart construction site safety control system includes: a plurality of monitoring devices, each of which is sequentially arranged along the construction site of the construction site. The construction site is divided into a plurality of detection and control areas based on the types of construction sites, and the monitoring devices in each detection and control area are all used to obtain monitoring data;

[0009] The comprehensive environment and safety management method of the smart construction site includes:

[0010] Obtain control index data, and the control index data acts on the monitoring devices in each of the detection and control areas;

[0011] Obtain the historical monitoring data in each detection and control area, and generate a plurality of preset thresholds based on the control index data and the historical monitoring data;

[0012] Obtain the real-time monitoring data in each detection and control area;

[0013] Based on the real-time monitoring data and the preset thresholds, generate a plurality of safety warning schemes corresponding to the types of detection and control areas;

[0014] Obtain the difference data between each safety warning scheme;

[0015] Adjust the control authority of the monitoring devices in the detection and control area based on the difference data type and value.

[0016] As a further preference of this technical solution, the method for the control index data to act on the monitoring devices in each of the detection and control areas includes:

[0017] Obtain the index values in the control index data based on the types of construction sites;

[0018] Associate the index data with the types of construction sites and generate control data;

[0019] Compare the types of monitoring data obtained by each of the monitoring devices, and obtain the difference monitoring data. Each of the monitoring devices has different types of difference monitoring data;

[0020] Associate the control index data with the monitoring devices based on the difference monitoring data type and the control data type.

[0021] As a further preference of this technical solution, the method for generating a plurality of preset thresholds based on the control index data and the historical monitoring data includes:

[0022] Based on the types of detection control regions, the historical monitoring data and control index data are projected into a two-dimensional coordinate axis. The abscissa of the two-dimensional coordinate axis is time, and the ordinate of the two-dimensional coordinate axis is the data value;

[0023] The historical monitoring data of different types of detection control regions form different types of fitting curves in the two-dimensional coordinate axis;

[0024] The control index data forms a straight line parallel to the abscissa in the two-dimensional coordinate axis;

[0025] Obtain the data values of the intersection points of the straight line and the fitting curves, and generate multiple preset thresholds in combination with the types of fitting curves.

[0026] As a further optimization of this technical solution, the method of obtaining the data values of the intersection points of the straight line and the fitting curves and generating multiple preset thresholds in combination with the types of fitting curves can also be replaced by:

[0027] Retrieve the intersection points of the straight line and the fitting curves. Taking the intersection points as boundaries, obtain multiple coordinate points in the fitting curves that exceed the boundaries, and based on the multiple coordinate points, obtain the slopes of the parts of the fitting curves that exceed the boundaries. The slopes of different types of fitting curves, in combination with the types of fitting curves, generate multiple preset thresholds.

[0028] As a further optimization of this technical solution, the method for generating a safety warning plan includes:

[0029] Obtain the absolute value of the numerical difference between the real-time monitoring data and the preset thresholds;

[0030] Manually set the accuracy index, and based on the accuracy index and the absolute value of the numerical difference, divide the safety warning plan into different levels, such as: minor, medium, and severe;

[0031] Extract the data characteristics of the absolute value of the numerical difference and associate them with the safety warning plan levels;

[0032] Assign weights based on the warning plan levels to the data items in the monitoring device that correspond to the data characteristics.

[0033] As a further optimization of this technical solution, the method for obtaining the difference data between each safety warning plan includes:

[0034] Based on the types of safety warning plans, fill the data values and types in the safety warning plans into a difference comparison table. The rows and columns of the difference comparison table correspond to different types of safety warning plans respectively;

[0035] Calculate the differences between the data values corresponding to the rows and columns in the difference comparison table to generate a difference data matrix;

[0036] The matrix data in the difference data matrix that matches the safety warning plan types is the difference data.

[0037] As a further preference of this technical solution, the method for adjusting the control authority of the monitoring devices in the detection control area based on different data types and values includes:

[0038] Associate different data types with the types of detection control areas;

[0039] Obtain the values of the different data, and determine the priority of management authority adjustment based on the magnitudes of the values;

[0040] Divide the management authority into multiple levels, for example: high, medium, and low, with each level corresponding to different monitoring device control authorities;

[0041] Based on the priority and the magnitudes of the values, determine the monitoring devices for which the authority needs to be adjusted, and generate corresponding authority adjustment plans;

[0042] Send the authority adjustment plans to the monitoring devices in the detection control area.

[0043] As a further preference of this technical solution, the method for the monitoring devices to obtain monitoring data includes:

[0044] Obtain the first monitoring data frequency, where the first monitoring data frequency is the average value of the monitoring data frequencies obtained by the monitoring devices;

[0045] Obtain the second monitoring data frequency, where the second monitoring data frequency is the average value of the monitoring data frequencies obtained by the monitoring devices after they execute the obtaining of the control index data;

[0046] Based on the first monitoring data frequency and the second monitoring data frequency, construct a confidence discrimination rule;

[0047] The confidence discrimination rule acts on the monitoring devices and outputs monitoring data.

[0048] In a second aspect, to improve the above technical solution, a smart construction site safety control system based on sensor monitoring is proposed, which uses the above-disclosed smart construction site safety control method based on sensor monitoring and includes:

[0049] A monitoring device network, composed of multiple monitoring devices, for obtaining real-time monitoring data related to the environment and safety in the detection control area;

[0050] A data acquisition and control module, for obtaining and processing control index data and applying it to the monitoring devices in each detection control area;

[0051] A real-time data monitoring module, for obtaining real-time monitoring data from the monitoring devices in each detection control area;

[0052] A safety warning generation module, which is used to monitor data in real time and generate a safety warning plan based on preset thresholds, including dividing the warning plan into different levels;

[0053] A differential data analysis module, which is used to obtain the differential data between different safety warning plans, and analyze and adjust the control permissions of the monitoring devices within the detection and control area;

[0054] A permission adjustment module, which is used to adjust the control permissions of the monitoring devices according to the types and values of the differential data;

[0055] A data correction and frequency management module, which is used to compare and adjust the acquisition frequency of the monitoring data, and correct the real-time data using the confidence discrimination rule;

[0056] A user interface and notification system, which is used to send safety warning information and permission adjustment notifications to relevant personnel through the user interface.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] The comprehensive environment and safety management method and system for the intelligent construction site can quickly generate a targeted safety warning plan by combining real-time monitoring data and preset thresholds, thereby improving the efficiency and accuracy of construction site safety management;

[0059] In addition, by obtaining the real-time monitoring data within each detection and control area, the system can timely discover potential safety hazards and avoid accidents. By obtaining the differential data between each safety warning plan, it can compare and analyze the safety conditions of different detection and control areas, and thus adjust the control permissions of the monitoring devices in a targeted manner to ensure the reasonable allocation and use of resources;

[0060] It should be added that the comprehensive environment and safety management system can timely send safety warning information and permission adjustment notifications to relevant personnel through the user interface and notification system, ensuring that relevant personnel can quickly take measures and improving the overall safety management level of the construction site. Description of the Drawings

[0061] Figure 1 Is the flowchart of the disclosed method of the present invention;

[0062] Figure 2 Is the auxiliary explanatory diagram of step S200 of the present invention;

[0063] Figure 3 Is the composition diagram of the disclosed system of the present invention. Detailed Embodiments

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] Before delving into the implementation manners proposed in this application, it is first necessary to clarify that in the prior art, data collection for large construction sites usually relies on the setting of a monitoring device network. In the prior art, the monitoring device network consists of multiple monitoring devices, which are arranged in sequence along the construction sites of the construction site, thereby subdividing the large construction site into multiple detection and control areas. The monitoring devices within each detection and control area are responsible for collecting different types of monitoring data.

[0066] For example: The detection and control areas are divided into the entrance and exit of the large construction site, the construction equipment concentration area, the material stacking area, and the construction operation area. It should be noted that according to the specific requirements of each area, different types of monitoring devices will be configured. For example: The entrance and exit area may require the installation of a license plate recognition system and a personnel identity verification device, while the construction equipment concentration area may require equipment operation status monitoring devices and position tracking devices.

[0067] Furthermore, the monitoring data collected by the monitoring device network in the present invention is usually transmitted to the intelligent management center of the smart construction site. In practical applications, the intelligent management center of the smart construction site is the control room in the office building of the construction site. It should be emphasized that the methods and systems disclosed in the present invention are applied to the control computer in the control room of the office building of the construction site.

[0068] In view of the fact that there are multiple detection and control areas in a large construction site, and the required safety warning schemes and monitoring device control authorities for each area are different, therefore, when conducting safety management, it is necessary to consider the actual needs of each detection and control area. Based on this, this application proposes a smart construction site safety control method based on sensor monitoring, as Figure 1 shown, this method includes step S100 to step S600.

[0069] Step S100: Obtain control index data.

[0070] It should be clear that the control index data acts on the monitoring devices within each detection and control area, and in the application, the control index data is set artificially. The artificial setting of the control index data is to ensure that it can effectively measure and monitor its business performance, goal achievement, and the need for continuous improvement. By setting specific indicators, the expected results are clarified, providing clear work directions and goals for employees.

[0071] In addition, it should be supplemented that in step S100: obtaining control index data, in the prior art, it is possible to manually input control index data by setting a transmission key. For example, in an actual scenario, by setting the transmission key as a USB flash drive to unlock the device or the computer system of the control center, at this time, the operator can input or adjust the control index data to the device or the computer system of the control center. It should be further explained that the control index data can be numerical data, such as temperature, humidity, and vibration intensity, or it can also be status data, such as the operating status of the device and the result of personnel identity verification. For example, in the detection and control area facing the entrance and exit, the management index is the accuracy and speed of license plate recognition or personnel identity recognition.

[0072] In addition, it should be further explained that the method for the control index data to act on the monitoring devices in each detection and control area includes steps S110 - S140.

[0073] Step S110: Obtain the index value based on the type of construction site in the control index data.

[0074] It should be clear that step S110 is used to determine the specific requirements of each detection and control area and the configuration of the monitoring devices. For example, in the construction equipment concentration area, the index values may include the normal range of the equipment operating status and the threshold of the equipment failure rate, while in the material stacking area, the index values may focus on humidity and temperature to ensure that the storage conditions of the materials meet the requirements.

[0075] Specifically, when the specific content of the control index data is "allow workers with a height of 175 cm or more to work", the index value is 175 cm.

[0076] Step S120: Associate the index data with the type of construction site and generate multiple control data.

[0077] It should be clear that step S120 is used to associate the control index data with a specific type of construction site, so as to generate specific control data applicable to different detection and control areas.

[0078] Specifically, when the construction site is the "detection and control area of the entrance and exit" and the specific content of the control index data is "allow workers with a height of 175 cm or more to work", the control data is "allow workers with a height of 175 cm or more to pass through the detection and control area of the entrance and exit".

[0079] Step S130: Compare the types of monitoring data obtained by each monitoring device and obtain the differential monitoring data.

[0080] It should be clear that in step S130, each monitoring device has different types of differential monitoring data.

[0081] It should also be supplemented that step 130 is used to determine whether each monitoring device can meet the specific requirements of the corresponding detection control area. For example, if the detection control area requires monitoring the operating status of equipment, and a certain monitoring device can only provide temperature data, then this device cannot meet the requirements and needs to be adjusted or replaced.

[0082] Specifically, for example, compare the types of monitoring data obtained by the monitoring devices in the "detection control area of the entrance and exit" and "the concentrated area of construction equipment".

[0083] It should be noted that the types of monitoring data obtained by the monitoring devices in the "detection control area of the entrance and exit" include license plate recognition results and personnel identity verification results, while the types of monitoring data obtained by the monitoring devices in the "concentrated area of construction equipment" include equipment operating status and location tracking information. By comparing these data types, the obtained differential monitoring data type is "the set between license plate recognition results and personnel identity verification results and equipment operating status and location tracking information".

[0084] Step S140: Associate the control index data and the monitoring devices based on the differential monitoring data type and the control data type.

[0085] It should be clear that step S140 is used to ensure that each monitoring device can match the corresponding control index data according to the data type it obtains, so as to achieve effective monitoring and management.

[0086] Specifically, step S140 associates the output of the monitoring device with the control data of the specific requirements of the detection control area to ensure that the monitoring device can provide the required information to meet the requirements of the management indicators.

[0087] For example, in the "detection control area of the entrance and exit", if the control data requires that "workers with a height of more than 175 cm are allowed to pass", then it is necessary to associate the monitoring data of the height measurement device with this control data. In this way, when the monitoring device detects the height data of the worker, the system can automatically determine whether the worker meets the requirements of the management indicators and make corresponding control decisions, such as allowing or refusing them to pass. In addition, in the "concentrated area of construction equipment", if the control data requires that "the equipment operating status is normal and the failure rate is lower than the set threshold", then it is necessary to associate the monitoring data of the equipment status monitoring device with this control data. In this way, when the monitoring device detects that the equipment status is abnormal, the system can issue an alarm in time and take measures, such as suspending the operation of the equipment or notifying the maintenance personnel for repair, to ensure that the equipment operates within a safe range.

[0088] Through the above steps, the comprehensive environment and safety management method for intelligent construction sites can achieve precise monitoring and management of different detection and control areas, ensuring the safe, efficient, and orderly operation of construction sites. In addition, it can flexibly adjust the control index data and the configuration of monitoring devices according to the actual operation situation and changes in management requirements to adapt to the ever-changing working environment and conditions.

[0089] Step S200: Obtain the historical monitoring data within each detection and control area, and generate multiple preset thresholds based on the control index data and the historical monitoring data.

[0090] It should be added that in this application, the method for the monitoring device to obtain monitoring data includes Step S201 - Step S205.

[0091] Step S201: Obtain the first monitoring data frequency, where the first monitoring data frequency is the average value of the monitoring data frequencies obtained by the monitoring device.

[0092] It should be noted that the first monitoring data frequency reflects the data acquisition ability of the monitoring device within a certain period of time, which helps to evaluate its performance and reliability. For example: If the monitoring device is used to monitor the operating status of equipment, then its data frequency may need to be relatively high to ensure that any abnormal situations during equipment operation can be captured in a timely manner.

[0093] Step S202: Obtain the second monitoring data frequency, where the second monitoring data frequency is the average value of the monitoring data frequencies obtained by the monitoring device after executing the acquisition of control index data.

[0094] It should be clear that the change in the second monitoring data frequency can reflect the data acquisition ability of the monitoring device under specific tasks or conditions, which helps to evaluate its response speed and adaptability to the control index data. For example: If the data frequency of the monitoring device significantly increases after obtaining the control index data, this may indicate that the device can quickly adjust its acquisition frequency to meet the new monitoring requirements.

[0095] Step S203: Based on the first monitoring data frequency and the second monitoring data frequency, construct a confidence discrimination rule.

[0096] It should be clear that the construction of the confidence discrimination rule is to ensure that the data acquisition ability of the monitoring device under different conditions can meet the predetermined performance standards. By obtaining the first monitoring data frequency and the second monitoring data frequency as the confidence discrimination rule, it can be determined whether the monitoring device shows sufficient flexibility and adaptability when performing specific tasks.

[0097] It should be added that the confidence discrimination rule is the range frequency between the first monitoring data frequency and the second monitoring data frequency.

[0098] Step S204: The confidence discrimination rule acts on the monitoring device and outputs monitoring data.

[0099] It should be noted that the real-time monitoring data obtained by the monitoring device, after being discriminated by the confidence discrimination rule, can identify whether the obtained monitoring data can be collected as abnormal data, which improves the accuracy and reliability of the monitoring data to a certain extent.

[0100] Specifically, for example: If the data frequency of the monitoring device significantly decreases after performing a specific task, this may indicate that there are performance bottlenecks or faults in the device when dealing with new tasks. At this time, the confidence discrimination rule will help to identify and exclude these abnormal data to ensure the accuracy of subsequent analysis and decision-making.

[0101] It should be clear that the method of generating multiple preset thresholds based on the control index data and historical monitoring data in step S200 includes: step S210 - step S240.

[0102] Step S210: Based on the types of detection control regions, project the historical monitoring data and control index data into a two-dimensional coordinate axis.

[0103] It should be clear that step S210 is used to visually display the historical monitoring data and control index data to better understand the relationship and trend between the data. A two-dimensional coordinate axis is constructed through a computer image programming program in the prior art, and the historical monitoring data and control index data are projected into the two-dimensional coordinate axis to intuitively observe the performance of different detection control regions on specific indicators. In addition, it is known that the abscissa of the two-dimensional coordinate axis is time, and the ordinate of the two-dimensional coordinate axis is the data value. Figure 2 It can be known that the abscissa of the two-dimensional coordinate axis is time, and the ordinate of the two-dimensional coordinate axis is the data value.

[0104] Step S220: The historical monitoring data of different types of detection control regions form different types of fitting curves in the two-dimensional coordinate axis.

[0105] It should be clear that step S220 is used to analyze the distribution of historical monitoring data in the two-dimensional coordinate axis to form different types of fitting curves to reveal the trend and law of data change over time. For example: The fitting curve representing the "detection control region of the entrance and exit" may show the change trend of the accuracy of personnel identity recognition over time. The fitting curve representing the "material stacking area" may reflect the changes in humidity and temperature. It should be added that Figure 2 where A1 - A4 are different types of fitting curves respectively.

[0106] Step S230: The control index data forms a straight line parallel to the abscissa in the two-dimensional coordinate axis.

[0107] It should be clear that step S230 is used to display the control index data in a two-dimensional coordinate axis in an intuitive manner, forming a straight line parallel to the abscissa. Specifically, referring to Figure 2 it can be known as line B. It should be added that in actual operation, there can be multiple lines B, and each line B corresponds to a fitting curve.

[0108] Step S240: Obtain the data values of the intersection points of the straight line and the fitting curve, and generate multiple preset thresholds in combination with the types of fitting curves.

[0109] It should be clear that step S240 is used to determine the numerical range of the preset threshold.

[0110] Specifically, by analyzing the intersection points of the straight line formed by the control index data and the fitting curve of the historical monitoring data, the reasonable fluctuation range of the management index within a specific time period can be determined. For example: If the control index data requires that "the equipment is operating normally and the failure rate is lower than the set threshold", then the reasonable fluctuation range of the equipment failure rate can be determined by analyzing the intersection points of the fitting curve of the historical monitoring data in the "construction equipment concentration area" and the straight line of the control index data. In this way, when the real-time monitoring data exceeds this range, the system will automatically trigger the warning mechanism to remind the management personnel to take corresponding measures to ensure that the equipment operates within the safe range.

[0111] It should be added that in the specific implementation, there is also an alternative way of step S241 for step S240.

[0112] Step S241: Retrieve the intersection points of the straight line and the fitting curve. Taking the intersection points as the boundaries, obtain multiple coordinate points within the fitting curve that exceed the boundaries, and based on the multiple coordinate points, obtain the slope of the part of the fitting curve that exceeds the boundaries. The slopes of different types of fitting curves, combined with the types of fitting curves, generate multiple preset thresholds.

[0113] It should be clear that compared with step S240, step S241 focuses more on extracting information from the dynamic changes of the fitting curve. By analyzing the slope of the part of the fitting curve that exceeds the boundaries, accurately understand the fluctuation trend of the control index data. For example: In the monitoring of the equipment failure rate, if the slope of the fitting curve suddenly increases after the intersection point, this may indicate that the failure rate is rising rapidly, and the system needs to issue a warning immediately. On the contrary, if the slope decreases, it indicates that the failure rate is decreasing, and the system can adjust the warning level accordingly.

[0114] In addition, in step S241, by retrieving coordinate points beyond the limit, the system can identify abnormal fluctuations that may have been overlooked, thereby improving the sensitivity and accuracy of the early warning system. For example, in the monitoring of the "construction equipment concentration area", if the slope of the equipment failure rate suddenly increases within a certain period of time, even if the failure rate has not exceeded the preset threshold, the system can issue an early warning in advance based on the slope change trend, providing reaction time for the management personnel.

[0115] It should be added that step S241 also allows the system to generate multiple preset thresholds according to different types of fitting curves, which means that the system can formulate more personalized early warning strategies according to different equipment types, working environments or historical data characteristics. For example, for old equipment, the system may set a more stringent failure rate threshold, while for new equipment, a relatively loose threshold may be adopted to adapt to their different operating characteristics.

[0116] In summary, while ensuring the rationality of the fluctuation range of the control index data, step S241 further improves the sensitivity and personalization level of the early warning system by analyzing the slope change of the fitting curve. This not only helps to detect potential problems in a timely manner, but also provides more accurate decision-making support for the management personnel to ensure that the equipment operates in the best state.

[0117] Step S300: Obtain the real-time monitoring data in each detection control area.

[0118] It should be clear that step S300 is used to obtain the real-time monitoring data in each detection control area for comparison with the preset threshold to ensure the timely discovery of abnormal situations and the adoption of corresponding measures.

[0119] Step S400: Generate multiple safety early warning schemes corresponding to the types of detection control areas based on the real-time monitoring data and the preset threshold.

[0120] It should be clear that step S400 is used to generate multiple safety early warning schemes corresponding to the types of detection control areas to ensure that corresponding early warning measures can be taken quickly when the real-time monitoring data exceeds the preset threshold.

[0121] Specifically, the method for generating the safety early warning scheme in step S400 includes: step S410 - step S440.

[0122] Step S410: Obtain the absolute value of the numerical difference between the real-time monitoring data and the preset threshold.

[0123] It should be clear that step S410 can facilitate the quantification of the gap between the monitoring data and the threshold.

[0124] Specifically, by calculating the absolute value of the difference between the real-time monitoring data and the preset threshold, it is possible to clearly understand the degree of deviation of the current monitoring data from the safe range. For example, if the preset threshold is that the equipment failure rate does not exceed 5%, and the real-time monitoring data shows a failure rate of 7%, then the absolute value of the numerical difference is 2%, which indicates that the operating state of the equipment has deviated from the safe range and corresponding measures need to be taken.

[0125] Step S420: Manually set the accuracy index, and divide the safety warning plan into different levels based on the accuracy index and the absolute value of the numerical difference. For example: slight, medium, and severe.

[0126] It should be clear that step S420 can further refine the response measures for safety warnings. By manually setting the accuracy index and dividing the safety warning plan into different levels, it is convenient to more precisely handle various situations.

[0127] Specifically, the setting of the manually set accuracy index enables a more detailed warning plan to be provided according to the specific gap between the real-time monitoring data and the preset threshold during actual operation. For example, if the accuracy index is set to 1%, then when the absolute value of the numerical difference is within 1%, the warning plan can be classified as a slight level; when the absolute value of the numerical difference is between 1% and 3%, the warning plan can be classified as a medium level; and when the absolute value of the numerical difference exceeds 3%, the warning plan is classified as a severe level. In this way, managers can take different levels of response measures according to the warning level, thereby improving the overall safety management level.

[0128] Step S430: Extract the data characteristics of the absolute value of the numerical difference and associate with the safety warning plan level.

[0129] It should be clear that step S430 can clearly associate the relationship between the monitoring data and the warning level, thereby providing an intuitive reference basis for decision-makers.

[0130] Specifically, by extracting the data characteristics of the absolute value of the numerical difference, such as "the application place of the detection and control area" and "the object of the obtained data", the division of the warning level can be further refined. For example, when the data characteristic of the absolute value of the numerical difference is "the detection and control area of the entrance and exit", it indicates that special attention needs to be paid to the safety status of this area because the entrance and exit are often places with high safety risks. If the data object is "the operating parameters of key equipment", it indicates that even small changes in these parameters may have a significant impact on the stable operation of the entire system. By associating these data characteristics with the warning plan level, it can ensure that when an abnormality occurs, the root cause of the problem can be quickly identified.

[0131] Step S440: Assign weights based on the warning scheme level to the data items corresponding to the data features within the monitoring device.

[0132] It should be clear that step S440 is to ensure that during safety warning, corresponding attention can be given according to different data features and their impact degrees on the overall safety condition.

[0133] Specifically, by assigning weights to each data item, the warning system can pay more attention to those data features that have a greater impact on safety when analyzing and processing monitoring data. For example: If a data item represents the operating parameters of a key device, then this data item may be assigned a higher weight because even a small change in these parameters may have a significant impact on the stable operation of the entire system. On the contrary, if a data item represents the environmental parameters of a non-critical area, then it may be assigned a lower weight because the change in these parameters has a relatively small impact on the overall safety.

[0134] It should be further noted that during actual operation, the function of assigning weights is implemented through algorithms and programs in the existing computer technology, ensuring the flexibility and adaptability of the warning system.

[0135] Step S500: Obtain the difference data between each safety warning scheme.

[0136] It should be clear that step S500 is used to obtain the difference data between each safety warning scheme to facilitate further optimizing the warning strategy and improving the accuracy of the warning system.

[0137] Specifically, by comparing the difference data between different safety warning schemes in step S500, redundancies or deficiencies in the warning strategy can be found. For example: If two warning schemes target similar monitoring data and thresholds, but there are significant differences in the warning levels, this may indicate that the warning strategy needs to be adjusted. By analyzing these difference data, the warning scheme can be optimized to ensure that the warning system can provide accurate and consistent responses in different situations.

[0138] It should be added that in the specific implementation of step S500, it is further divided into step S510 - step S530.

[0139] Step S510: Based on the types of safety warning schemes, fill the data values and types within the safety warning scheme into the difference comparison table.

[0140] It should be clear that the rows and columns of the difference comparison table correspond to different types of safety warning schemes respectively.

[0141] It should be added that the meaning of step S510 is to organize and classify the key data values in each security warning plan according to their types, so as to facilitate horizontal and vertical comparisons. In this way, the differences between different warning plans can be clearly shown, thus providing intuitive data support for subsequent optimization.

[0142] Specifically, in step S510, first, it is necessary to determine the structure of the difference comparison table to ensure that each row and each column correspond to a specific type of security warning plan. Then, the key data values in each plan, such as the absolute value of the numerical difference, the accuracy index, and the data characteristics, are filled into the corresponding cells according to the type of warning plan to which they belong. In this way, through the visual presentation of the table, it can be quickly identified which warning plans have significant differences in certain aspects, thus providing a basis for subsequent analysis and optimization.

[0143] Step S520: Calculate the differences between the data values in the corresponding rows and columns of the difference comparison table to generate a difference data matrix.

[0144] It should be clear that the meaning of step S520 is to generate a difference data matrix by calculating the differences between the data values in the corresponding rows and columns of the difference comparison table, further intuitively showing the degree of difference between different security warning plans.

[0145] Specifically, step S520 involves comparing the key data values of each warning plan one by one, calculating the differences between their absolute values of numerical differences, accuracy indexes, and data characteristics. For example, for two warning plans, the differences in their absolute values of numerical differences, as well as in accuracy indexes and data characteristics, can be calculated. In this way, a difference data matrix can be generated, where each element represents the degree of difference between two warning plans in a specific data characteristic.

[0146] In addition, when step S520 is specifically implemented, in order to ensure the accuracy and practicality of the difference data matrix, multiple mathematical methods can also be used for calculation, such as any one or two of Euclidean distance, Manhattan distance, or cosine similarity. It should be noted that these methods can quantify the differences between warning plans from different angles, thus providing a more scientific basis for subsequent analysis and optimization.

[0147] Step S530: The matrix data in the difference data matrix that matches the security warning plan type is the difference data.

[0148] It should be clear that the meaning of step S530 is to obtain differential data for further analysis and optimization of the early warning strategy. When implementing step S530, it is first necessary to ensure that each row and each column in the differential data matrix corresponds to a specific type of security early warning scheme. Then, by comparing the data values in the matrix, the differential points between different early warning schemes can be identified. For example, if the differential value between two early warning schemes is large for a certain data feature, this may indicate that the early warning strategy needs to be further optimized in this feature. In addition, in order to ensure the accurate matching of differential data, various methods can be used for data processing and analysis. For example, the clustering analysis method can be used to group early warning schemes with similar differential features into one category, so as to facilitate the identification of the commonalities and differences between early warning schemes. In addition, data mining techniques, such as association rule mining, can also be used to discover the potential connections and rules between different early warning schemes.

[0149] Step S600: Adjust the control authority of the monitoring devices in the detection control area based on the differential data type and value.

[0150] It should be clear that the meaning of step S600 is to dynamically adjust the control authority of the monitoring devices in the detection control area according to the differential data type and value to ensure the efficiency and accuracy of the early warning system. Specifically, by analyzing the differential data obtained in step S530, it can be identified which monitoring devices are more critical or sensitive in the early warning process.

[0151] It should be added that the implementation of step S600 needs to comprehensively consider factors such as the performance, location of the monitoring devices, and their association degree with key equipment. For example, if the differential value of a certain monitoring device for key data features is large, it indicates that it has a high importance in the early warning process. Therefore, the control authority of this device can be appropriately increased so that it can perform data collection and transmission more frequently, thereby improving the response speed and accuracy of the early warning system.

[0152] In addition, step S600 also involves the dynamic adjustment of the control authority of the monitoring devices, which means that the control authority of the monitoring devices may be different in different early warning situations. For example, during high-risk periods, the control authority of some key monitoring devices can be temporarily increased, while during low-risk periods, it can be appropriately reduced to save resources and reduce the unnecessary data processing burden.

[0153] It should also be noted that step S600 is further divided into steps S610 - S650 during specific implementation.

[0154] Step S610: Associate the differential data type with the detection control area type.

[0155] It should be clear that the meaning of step S610 is to associate the difference data types with specific types in the detection control area, so as to more precisely adjust the control authority of the monitoring device. Specifically, different detection control areas may have different requirements and sensitivities for the response of the early warning system. For example, a high-risk area may require more frequent data collection and faster early warning responses, while a low-risk area may not require such a high monitoring frequency.

[0156] In step S610, first, it is necessary to classify the detection control areas, determine the types and characteristics of each area. Then, match the difference data types obtained in step S530 with these area types to find out which difference data types are more important in specific area types. For example, for a high-risk area in a chemical plant, difference data types related to chemical leakage may need to be particularly concerned, while for a low-risk area in a residential area, difference data types related to noise or air quality may be more concerned.

[0157] It should be added that the difference data types and the detection control area types are associated and matched through a database in the computer system in the prior art. By writing corresponding algorithms and programs, the automatic association of the difference data types and the detection control area types can be achieved, thereby improving the efficiency and accuracy of the entire early warning system.

[0158] Step S620: Obtain the values of the difference data and determine the priority of the management authority adjustment based on the value size.

[0159] It should be noted that the meaning of step S620 is to determine the priority of the management authority adjustment according to the value size of the difference data, so as to more effectively optimize the early warning strategy. Specifically, the value of the difference data can reflect the degree of difference of the early warning plan in different data characteristics. The larger the value, the greater the difference between the performance of this early warning plan in this characteristic and other plans, and it may need to be adjusted preferentially.

[0160] In step S620, the retrieval program in the computer first sorts the values in the difference data matrix in step S520 to find the difference data with larger values. Then, according to the value size of these difference data, determine the priority of the management authority adjustment. The larger the value of the difference data, the higher the priority of the corresponding early warning plan adjustment.

[0161] For example: If there is a large difference in the absolute value of the numerical difference of a certain early warning plan, it indicates that there may be relatively large problems in the accuracy of the early warning. Therefore, it is necessary to preferentially adjust its control authority to ensure the accuracy and timeliness of the early warning system. And if there is a large difference in the accuracy index of a certain early warning plan, it may be necessary to preferentially adjust its data processing algorithm to improve the accuracy of the early warning plan.

[0162] To ensure the scientificity and rationality of the management authority adjustment, step S620 can also adopt various mathematical methods for auxiliary decision-making, such as the Analytic Hierarchy Process (AHP) or the Fuzzy Comprehensive Evaluation Method. Through these methods, the numerical size of the difference data and other relevant factors of the early warning plan can be comprehensively considered to determine a more reasonable priority for the management authority adjustment.

[0163] Step S630: Divide the management authority into multiple levels, for example: high, medium, and low, and each level corresponds to different monitoring device control authorities.

[0164] It should be clear that the meaning of step S630 is to divide the management authority into multiple levels to facilitate more detailed control of the monitoring device control authority. Specifically, the management authority can be divided into different levels such as high, medium, and low through the authority adjustment module in the computer program, and corresponding monitoring device control authorities can be set for each level to achieve fine management of the operation of the early warning system.

[0165] In step S630, first, the specific meaning of each authority level and the corresponding control authority need to be defined. For example: a high authority level may mean that the monitoring device can perform more frequent data collection, transmission, and analysis to ensure a rapid response in high-risk situations. The medium authority level may be between the high and low authority levels and is applicable to general risk situations. The low authority level means that the operation frequency and data processing volume of the monitoring device are relatively low and are applicable to low-risk periods.

[0166] Step S640: Determine the monitoring devices whose authorities need to be adjusted according to the priority and numerical size, and generate corresponding authority adjustment plans.

[0167] It should be clear that the meaning of step S640 is to determine the monitoring devices whose authorities need to be adjusted according to the priority and numerical size, and generate corresponding authority adjustment plans. Specifically, the computer can identify which monitoring devices need to adjust their control authorities first during the early warning process by analyzing the management authority adjustment priority determined in step S620 and the numerical size of the difference data. For example: if the difference value of a certain monitoring device in key data characteristics is large, it indicates that it has a high importance during the early warning process. Therefore, the control authority of this device can be appropriately increased to enable it to perform data collection and transmission more frequently, thereby improving the response speed and accuracy of the early warning system.

[0168] In step S640, the permission adjustment module in the computer first sorts the monitoring devices according to the priority and numerical value, and then generates corresponding permission adjustment plans. These plans will detail which monitoring devices need to increase or decrease the control permission, as well as the specific operating frequency and data processing volume after adjustment. For example, for high-priority monitoring devices, their control permission may need to be set to "high" to ensure that they can perform more frequent data collection and transmission; while for low-priority monitoring devices, their control permission may be set to "low" to reduce resource consumption and data processing burden.

[0169] Step S650: Send the permission adjustment plan to the monitoring devices within the detection control area.

[0170] It should be clear that the meaning of step S650 is to send the permission adjustment plan to the monitoring devices within the detection control area to facilitate the real-time update and optimization of the operating status of the early warning system. Specifically, through the permission adjustment module in the computer system, the permission adjustment plan generated in step S640 is transmitted to each monitoring device to ensure that each device can operate according to the latest permission adjustment plan.

[0171] It should be noted that to ensure security, in the actual implementation of step S650, the permission adjustment module first verifies the identity and permission of the monitoring devices to ensure the security and accuracy of information transmission. Subsequently, the permission adjustment plan is sent to each monitoring device in an encrypted form to prevent the data from being intercepted or tampered with during transmission. After receiving the permission adjustment plan, the monitoring device will automatically parse and execute the corresponding adjustment to ensure that its operating status is consistent with the requirements of the early warning system.

[0172] For example: If the control permission of a certain monitoring device is adjusted to "high", the device will immediately start a more frequent data collection and transmission mode to improve the response speed and accuracy of the early warning system. On the contrary, if the control permission of a certain monitoring device is adjusted to "low", its data collection and transmission frequency will be correspondingly reduced to reduce resource consumption and data processing burden.

[0173] In addition, to ensure the smooth implementation of the permission adjustment plan, step S650 can also adopt various technical means for assistance, such as using wireless communication technology, wired network or satellite communication to adapt to the communication requirements and environmental conditions of different detection control areas.

[0174] In addition, it should be noted that for the method proposed in this application, the processing schemes of all internal data are processed and analyzed by the computer.

[0175] Such as Figure 3As shown, this application introduces a smart construction site safety control system based on sensor monitoring. This system adopts a method for comprehensive environment and safety management of smart construction sites described above and integrates multiple key components.

[0176] First, the system has a built-in network of monitoring devices composed of numerous monitoring devices, which are responsible for collecting real-time data related to the environment and safety in the detection and control area.

[0177] Second, the system has a data acquisition and control module. Its main task is to obtain and process control index data and apply the processed data to the monitoring devices in each detection and control area. This process ensures that the monitoring devices can continuously receive the latest data, thereby improving the accuracy and efficiency of monitoring.

[0178] In addition, the system is also equipped with a real-time data monitoring module, which is responsible for extracting real-time monitoring data from the monitoring devices in each detection and control area, enabling managers to grasp the environmental and safety conditions of the construction site in real time.

[0179] To further enhance the safety of the construction site, the system also includes a safety warning generation module, which generates safety warning schemes of different levels based on real-time monitoring data and preset thresholds, so that managers can take corresponding measures according to the severity of the warning.

[0180] To optimize the system operation, the system also has a differential data analysis module, which is responsible for obtaining the differential data between different safety warning schemes, analyzing and processing it, and then adjusting the control permissions of the monitoring devices in the detection and control area to improve the overall operation efficiency of the system.

[0181] To enable the monitoring devices to operate under the best permissions, the system also includes a permission adjustment module, which adjusts the control permissions of the monitoring devices according to the type and value of the differential data to ensure the accuracy and reliability of the monitoring data.

[0182] To further improve the accuracy and reliability of the data, the system also has a data correction and frequency management module, which compares and adjusts the acquisition frequency of the monitoring data and corrects the real-time data using confidence discrimination rules to ensure that the obtained data is accurate and reliable, providing strong decision-making support for managers.

[0183] Finally, the system also includes a user interface and notification system, which sends safety warning information and permission adjustment notifications to relevant personnel through the user interface, ensuring that relevant personnel can timely understand the safety status of the construction site and the operation status of the system and take corresponding measures. This not only improves the safety of the construction site but also enhances the management efficiency.

[0184] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart construction site safety control method based on sensor monitoring, applied to a smart construction site safety control system, the smart construction site safety control system comprising: Multiple monitoring devices, each of which is arranged in sequence along the construction site of the construction site, and the construction site is divided into multiple detection control areas based on the type of construction site. The monitoring devices in each detection control area are used to obtain monitoring data, and are characterized by including: Acquire control index data, the control index data acting on the monitoring device in each of the detection control areas; Obtain historical monitoring data in each detection control area, and generate multiple preset thresholds based on control indicator data and historical monitoring data; Obtain real-time monitoring data within each detection and control area; Based on real-time monitoring data and preset thresholds, multiple safety warning plans corresponding to the types of detection and control areas are generated; Obtain the difference data between each safety warning scheme; Adjust the control authority of the monitoring device in the detection control area based on the difference data type and value; The method for generating multiple preset thresholds based on control indicator data and historical monitoring data includes: Based on the type of detection and control area, the historical monitoring data and control index data are projected into a two-dimensional coordinate axis, where the horizontal axis of the two-dimensional coordinate axis is time and the vertical axis of the two-dimensional coordinate axis is the data value; The historical monitoring data of different types of detection and control areas form different types of fitting curves in the two-dimensional coordinate axis; The control index data forms a straight line parallel to the horizontal axis in the two-dimensional coordinate axis; Obtain the data value of the interaction point between the straight line and the fitted curve, and generate multiple preset thresholds based on the type of the fitted curve; The method for obtaining the difference data between each safety warning scheme includes: Based on the type of safety warning scheme, fill the data value and type in the safety warning scheme into the difference comparison table, and the rows and columns of the difference comparison table correspond to different types of safety warning schemes; Calculate the differences between the data values ​​of corresponding rows and columns in the difference comparison table to generate a difference data matrix; The matrix data in the difference data matrix that matches the type of the safety warning scheme is the difference data; The method for adjusting the control authority of the monitoring device in the detection control area based on the difference data type and value includes: Associate the difference data type with the detection control area type; Obtain the value of the difference data, and determine the priority of management authority adjustment based on the value; Divide management authority into multiple levels, each level corresponds to different monitoring device control authority; According to the priority and value, determine the monitoring device that needs to adjust the authority, and generate the corresponding authority adjustment plan; The permission adjustment plan is sent to the monitoring device in the detection control area.

2. According to claim 1, a smart construction site safety control method based on sensor monitoring is characterized in that: The method of controlling the index data to act on the monitoring device in each detection control area includes: Obtain the index values ​​based on the type of construction site in the control index data; Associate indicator data with construction site types and generate control data; Comparing the types of monitoring data acquired by each of the monitoring devices, and acquiring differential monitoring data, each of the monitoring devices having different types of differential monitoring data; Control indicator data and monitoring devices are associated based on different monitoring data types and control data types.

3. According to claim 1, a smart construction site safety control method based on sensor monitoring is characterized in that: The method of obtaining the data value of the interaction point between the straight line and the fitted curve and generating multiple preset thresholds in combination with the type of the fitted curve can also be replaced by: Retrieve the interaction points between the straight line and the fitted curve, use the interaction points as boundaries, obtain multiple coordinate points in the fitted curve that exceed the boundaries, and obtain the slope of the portion of the fitted curve that exceeds the boundaries based on the multiple coordinate points. Different types of fitted curve slopes are combined with the types of fitted curves to generate multiple preset thresholds.

4. According to claim 1, a smart construction site safety control method based on sensor monitoring is characterized in that: The generation method of the safety warning plan includes: Obtain the absolute value of the difference between the real-time monitoring data and the preset threshold; Artificially set the accuracy index, and divide the safety warning scheme into different levels based on the accuracy index and the absolute value of the numerical difference; Extract the data features of the absolute value of the numerical difference and associate them with the level of the safety warning plan; The data items in the monitoring device and corresponding to the data features are given weights based on the level of the early warning scheme.

5. According to claim 1, a smart construction site safety control method based on sensor monitoring is characterized in that: The method for the monitoring device to obtain monitoring data includes: Obtaining a first monitoring data frequency, where the first monitoring data frequency is an average value of the monitoring data frequencies obtained by the monitoring device; Acquire a second monitoring data frequency, where the second monitoring data frequency is an average monitoring data frequency obtained after the monitoring device executes the acquisition of control indicator data; Constructing a confidence determination rule based on the first monitoring data frequency and the second monitoring data frequency; The confidence judgment rule acts on the monitoring device and outputs the monitoring data.

6. A smart construction site safety control system based on sensor monitoring, using a smart construction site safety control method based on sensor monitoring as described in any one of claims 1 to 5, characterized in that: include: A monitoring device network, consisting of multiple monitoring devices, is used to obtain real-time monitoring data related to the environment and safety within the detection and control area; Data acquisition and processing module, used to acquire and process control index data and apply it to the monitoring device of each detection and control area; A real-time data monitoring module is used to obtain real-time monitoring data from monitoring devices in each detection and control area; A safety warning generation module is used to generate safety warning plans based on real-time monitoring data and preset thresholds, including dividing the warning plans into different levels; The difference data analysis module is used to obtain the difference data between different safety warning schemes, and to analyze and adjust the control authority of the monitoring devices in the detection control area; The authority adjustment module is used to adjust the control authority of the monitoring device according to different data types and values; Data correction and frequency management module, used to compare and adjust the acquisition frequency of monitoring data, and correct the real-time data using confidence judgment rules; The user interface and notification system is used to send security warning information and permission adjustment notifications to relevant personnel through the user interface.

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