Intelligent construction site safety control method and system based on sensor monitoring

Abstract

The invention discloses an intelligent construction site safety control method and system based on sensor monitoring, and relates to the technical field of management monitoring. Comprising the steps of obtaining control index data, enabling the control index data to act on a monitoring device in each detection control area, obtaining 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 real-time monitoring data in each detection control area; and based on the real-time monitoring data and a preset threshold value, generating a plurality of safety early warning schemes corresponding to the types of the detection control areas, obtaining difference data between the safety early warning schemes, and based on the types and numerical values of the difference data, adjusting the control authority of the monitoring devices in the detection control areas. According to the invention, through the combination of the real-time monitoring data and the preset threshold value, a targeted safety early warning scheme can be quickly generated, so that the efficiency and accuracy of construction site safety management are improved.

Description

Technical Field

[0001] The present invention relates to the field of management and monitoring technology, and in particular to a smart construction site safety control method and system based on sensor monitoring. Background Art

[0002] There are not only various large-scale mechanical equipment but also a variety of building materials on construction sites. Therefore, safety accidents often occur on construction sites. For example, there are some dangerous areas on the construction site. If the workers are not aware of them, they may get into danger if they mistakenly enter the area. In addition, new employees do not have a strong sense of safety and are not familiar with various safety wearable equipment, which can easily lead to danger. Therefore, in order to fundamentally eliminate safety hazards, it is necessary to enhance their safety awareness through repeated training. However, this method not only has high training costs, but also has poor results. Therefore, a smart construction site safety control method and system based on sensor monitoring has appeared on the market.

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

[0004] In addition, the Chinese invention patent with publication number "CN113721564A" discloses an "intelligent workshop safety management system". This application realizes the automatic monitoring and response, recording, analysis and other management functions of workshop personnel, equipment, energy, disasters and other safety incidents through the personnel safety management subsystem, equipment safety management subsystem, video monitoring subsystem and control center, and conducts comprehensive intelligent safety management of the workshop to ensure that while the workshop's intelligence level is improved, safety issues are effectively resolved, and the workshop's production efficiency is further improved.

[0005] Since there are many detection and control areas in large construction sites, the safety warning plans and monitoring device control authorities required for each detection and control area are not the same. However, the above-mentioned disclosed patent method and similar methods cannot flexibly adapt to the needs of different detection and control areas and make corresponding warning plans during actual operation, resulting in low safety management efficiency. Summary of the invention

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

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

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

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

[0010] Acquire control index data, the control index data acting on the monitoring device in each of the detection control areas;

[0011] Obtain historical monitoring data in each detection control area, and generate multiple preset thresholds based on control indicator data and historical monitoring data;

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

[0013] Based on real-time monitoring data and preset thresholds, multiple safety warning plans corresponding to the types of detection and control areas are generated;

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

[0015] Adjust the control authority of the monitoring devices within the detection and control area based on the difference in data types and values.

[0016] As a further preferred embodiment of the present technical solution, the method for controlling the index data to act on the monitoring device in each detection control area includes:

[0017] Obtain the index values ​​based on the type of construction site in the control index data;

[0018] Associate indicator data with construction site types and generate control data;

[0019] 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;

[0020] Control indicator data and monitoring devices are associated based on different monitoring data types and control data types.

[0021] As a further preferred embodiment of the present technical solution, a method for generating multiple preset thresholds based on control indicator data and historical monitoring data includes:

[0022] 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;

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

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

[0025] The data values ​​of the interaction points between the straight line and the fitted curve are obtained, and multiple preset thresholds are generated based on the type of the fitted curve.

[0026] As a further preferred embodiment of the present technical solution, the method of obtaining the data value of the interaction point between the straight line and the fitting curve and generating multiple preset thresholds in combination with the type of fitting curve can also be replaced by:

[0027] 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.

[0028] As a further preferred embodiment of the present technical solution, a method for generating a safety warning solution includes:

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

[0030] Artificially set accuracy indicators, and divide safety warning plans into different levels based on accuracy indicators and absolute values ​​of numerical differences, such as: minor, moderate, and severe;

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

[0032] 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.

[0033] As a further preferred embodiment of the present technical solution, a method for obtaining difference data between each safety warning scheme includes:

[0034] 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;

[0035] Calculate the differences between the data values ​​of corresponding 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 type of the safety warning scheme is the difference data.

[0037] As a further preferred embodiment of the present technical solution, a method for adjusting the control authority of a monitoring device in a detection control area based on difference data types and values ​​includes:

[0038] Associate the difference data type with the detection control area type;

[0039] Obtain the value of the difference data, and determine the priority of management authority adjustment based on the value;

[0040] Divide management permissions into multiple levels, such as high, medium, and low, and each level corresponds to different monitoring device control permissions;

[0041] According to the priority and value, determine the monitoring device that needs to adjust the authority, and generate the corresponding authority adjustment plan;

[0042] The permission adjustment plan is sent to the monitoring device in the detection control area.

[0043] As a further preferred embodiment of the present technical solution, the method for the monitoring device to obtain monitoring data includes:

[0044] 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;

[0045] 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;

[0046] Constructing a confidence determination rule based on the first monitoring data frequency and the second monitoring data frequency;

[0047] The confidence judgment rule acts on the monitoring device and outputs the monitoring data.

[0048] In the second aspect, in order 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, 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;

[0050] Data acquisition and control module, used to acquire and process control index data and apply it to the monitoring device of each detection and control area;

[0051] A real-time data monitoring module is used to obtain real-time monitoring data from monitoring devices in each detection and control area;

[0052] 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;

[0053] 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;

[0054] The authority adjustment module is used to adjust the control authority of the monitoring device according to different data types and values;

[0055] 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;

[0056] The user interface and notification system is used to send security warning information and permission adjustment notifications to relevant personnel through the user interface.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The integrated environment and safety management method and system for smart construction sites can quickly generate targeted safety warning plans through the combination of real-time monitoring data and preset thresholds, thereby improving the efficiency and accuracy of construction site safety management;

[0059] In addition, by acquiring real-time monitoring data in each detection and control area, the system can promptly discover potential safety hazards and avoid accidents. By acquiring the difference data between each safety warning scheme, it can conduct comparative analysis of the safety status of different detection and control areas, thereby adjusting the control authority of the monitoring device in a targeted manner and ensuring the rational allocation and use of resources.

[0060] It should be added that the integrated environment and safety management system can send safety warning information and authority adjustment notifications to relevant personnel in a timely manner through the user interface and notification system, ensuring that relevant personnel can take measures quickly to improve the overall safety management level of the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0062] Figure 2 is an auxiliary illustration of step S200 of the present invention;

[0063] Figure 3 It is a composition diagram of the disclosed system of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Before discussing in depth the implementation methods proposed in the present 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 is composed of a plurality of monitoring devices, which are arranged in sequence along the construction site of the construction site, thereby subdividing the large construction site into a plurality of detection control areas. The monitoring devices in each detection control area are responsible for collecting different types of monitoring data.

[0066] For example: the detection and control area is divided into the entrances and exits of large construction sites, construction equipment concentration areas, material storage areas and construction work areas. It should be pointed out that different types of monitoring devices will be configured according to the specific needs of each area. For example: the entrance and exit areas may need to install license plate recognition systems and personnel identity verification devices, while the construction equipment concentration area may require equipment operation status monitoring devices and location 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 actual 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 method and system 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 large construction sites, each area requires different safety warning solutions and monitoring device control permissions. Therefore, when conducting safety management, the actual needs of each detection and control area must be taken into account. Based on this, this application proposes a smart construction site safety control method based on sensor monitoring, such as Figure 1 As shown, the method includes steps S100 to S600.

[0069] Step S100: Acquire control index data.

[0070] It should be clear that the control indicator data is applied to the monitoring devices in each detection and control area, and in the application, the control indicator data is set manually. The purpose of manually setting the control indicator data is to ensure that its business performance, goal achievement and the need for continuous improvement can be effectively measured and monitored. By setting specific indicators and clarifying the expected results, clear work directions and goals are provided to employees.

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

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

[0073] Step S110: Obtaining index values ​​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 needs and configuration of the monitoring device for each detection and control area. For example, in the construction equipment concentration area, the indicator value 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 indicator value 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 indicator data is "allowing workers with a height of more than 175 cm to work", the indicator value is 175 cm.

[0076] Step S120: Associating the index data with the type of construction site and generating a plurality of control data.

[0077] It should be understood that step S120 is used to associate the control index data with a specific type of construction site, thereby generating specific control data applicable to different detection control areas.

[0078] Specifically, when the construction site is the "entry and exit detection and control area" and the specific content of the control indicator data is "allowing workers with a height of over 175 cm to work", the control data is "allowing workers with a height of over 175 cm to pass through the entry and exit detection and control area".

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

[0080] It should be understood that, in step S130, each monitoring device has different types of difference monitoring numbers.

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

[0082] Specifically, for example: comparing the types of monitoring data obtained by the monitoring device in the "entry and exit detection control area" and "in the construction equipment concentration area".

[0083] It should be noted that the monitoring data types obtained by the monitoring device in the "entry and exit detection and control area" include: license plate recognition results and personnel identity authentication results, while the monitoring data types obtained by the monitoring device in the "construction equipment concentration area" include equipment operation status and location tracking information. By comparing these data types, the difference monitoring data type obtained is "the set of license plate recognition results and personnel identity authentication results with equipment operation status and location tracking information."

[0084] Step S140: Associating control indicator data with monitoring devices based on the difference monitoring data type and control data type.

[0085] It should be clear that step S140 is used to ensure that each monitoring device can match the data type it obtains with the corresponding control indicator data, thereby achieving 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 "entry and exit detection control area", if the control data requires "allowing workers with a height of more than 175cm to pass", the monitoring data of the height measurement device needs to be associated with the 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 his passage. In addition, in the "construction equipment concentration area", if the control data requires "the equipment is in normal operating status and the failure rate is lower than the set threshold", the monitoring data of the equipment status monitoring device needs to be associated with the 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 equipment operation or notifying maintenance personnel to carry out maintenance, to ensure that the equipment operates within a safe range.

[0088] Through the above steps, the comprehensive environment and safety management method of the smart construction site can realize the precise monitoring and management of different detection and control areas, ensuring the safe, efficient and orderly operation of the construction site. In addition, it can also flexibly adjust the control indicator data and the configuration of the monitoring device according to the actual operating conditions and changes in management needs to adapt to the ever-changing working environment and conditions.

[0089] Step S200: Acquire historical monitoring data in each detection control area, and generate multiple preset thresholds based on control indicator data and historical monitoring data.

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

[0091] Step S201: Acquire a first monitoring data frequency, where the first monitoring data frequency is an average value of the monitoring data frequencies acquired by the monitoring device.

[0092] It should be noted that the first monitoring data frequency reflects the data collection capability 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 the equipment, then its data frequency may need to be higher to ensure that any abnormal conditions in the operation of the equipment can be captured in a timely manner.

[0093] Step S202: obtaining a second monitoring data frequency, where the second monitoring data frequency is an average value of the monitoring data frequency obtained after the monitoring device executes the acquisition of control indicator data.

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

[0095] Step S203: constructing a confidence determination rule based on the first monitoring data frequency and the second monitoring data frequency.

[0096] It should be clear that the confidence judgment rules are constructed to ensure that the data collection capabilities 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 confidence judgment rules, it can be determined whether the monitoring device exhibits sufficient flexibility and adaptability when performing specific tasks.

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

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

[0099] It should be noted that the real-time monitoring data acquired by the monitoring device can be identified as abnormal data after being judged by the confidence judgment rules, thereby improving the accuracy and reliability of the monitoring data to a certain extent.

[0100] Specifically, for example: if the data frequency of a monitoring device drops significantly after performing a specific task, this may indicate that the device has a performance bottleneck or failure when responding to a new task. At this time, the confidence judgment rule will help identify and exclude these abnormal data to ensure the accuracy of subsequent analysis and decision-making.

[0101] It should be understood that the method for generating a plurality of preset thresholds based on control indicator data and historical monitoring data in step S200 includes: steps S210 to S240.

[0102] Step S210: Based on the detection control area type, the historical monitoring data and control indicator data are projected into a two-dimensional coordinate axis.

[0103] It should be clear that step S210 is used to visualize 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 and control areas on specific indicators. In addition, reference Figure 2 It can be seen that 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.

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

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

[0106] Step S230: Control the index data to form a straight line parallel to the horizontal axis 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 to form a straight line parallel to the horizontal axis. Figure 2 It can be seen that it is straight line B. It should be added that in actual operation, there can be multiple straight lines B, and each straight line B corresponds to a fitting curve.

[0108] Step S240: obtaining data values ​​of the interaction points between the straight line and the fitting curve, and generating a plurality of preset thresholds in combination with the type of the fitting curve.

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

[0110] Specifically, by analyzing the interaction points between the straight line formed by the control index data and the fitting curve of the historical monitoring data, it is possible to determine the reasonable fluctuation range of the management indicator within a specific time period. For example, if the control index data requires that "the equipment is in normal operating status and the failure rate is lower than the set threshold", the reasonable fluctuation range of the equipment failure rate can be determined by analyzing the interaction points between the fitting curve of the historical monitoring data of 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 early warning mechanism to remind management personnel to take corresponding measures to ensure that the equipment operates within a safe range.

[0111] It should be added that, in a specific implementation, step S240 may be an alternative to step S241.

[0112] Step S241: Retrieve the interaction points between the straight line and the fitting curve, use the interaction points as boundaries, obtain multiple coordinate points in the fitting curve that exceed the boundaries, and obtain the slope of the fitting curve that exceeds the boundaries based on the multiple coordinate points. Different types of fitting curve slopes are combined with the type of fitting curve to 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 fitting curve that exceeds the limit, the fluctuation trend of the control index data can be accurately understood. For example: in equipment failure rate monitoring, if the slope of the fitting curve suddenly increases after the interaction point, this may indicate that the failure rate is rising rapidly and the system needs to issue an early warning immediately. On the contrary, if the slope decreases, it indicates that the failure rate is decreasing, and the system can adjust the early warning level accordingly.

[0114] In addition, in step S241, by retrieving coordinate points that exceed the limit, the system can identify abnormal fluctuations that may be 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 also issue an early warning based on the slope change trend, thereby providing management personnel with reaction time.

[0115] It should be added that step S241 also allows the system to generate multiple preset thresholds based on different types of fitting curves, which means that the system can formulate more personalized early warning strategies based on 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, it may adopt a relatively loose threshold to adapt to its different operating characteristics.

[0116] In summary, step S241, while ensuring the rationality of the fluctuation range of the control index data, 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 discover potential problems in a timely manner, but also provides managers with more accurate decision-making support to ensure that the equipment operates in the best condition.

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

[0118] It should be clear that step S300 is used to obtain real-time monitoring data in each detection control area for comparison with a preset threshold value to ensure that abnormal situations are discovered in time and corresponding measures are taken.

[0119] Step S400: Based on the real-time monitoring data and the preset threshold, a plurality of safety warning schemes corresponding to the types of detection control areas are generated.

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

[0121] Specifically, the method for generating the safety warning plan in step S400 includes: step S410 to step S440.

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

[0123] It should be clear that step S410 can facilitate quantifying 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, the degree of deviation of the current monitoring data from the safe range can be clearly understood. For example, if the preset threshold is that the equipment failure rate does not exceed 5%, and the real-time monitoring data shows that the failure rate is 7%, then the absolute value of the numerical difference is 2%, which indicates that the equipment operating status 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 scheme 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 of the security warning by manually setting accuracy indicators and dividing the security warning scheme into different levels, so as to respond to various situations more accurately.

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

[0128] Step S430: extracting data features of the absolute value of the numerical difference and associating them with the safety warning scheme 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 for decision makers.

[0130] Specifically, by extracting data features of the absolute value of numerical differences, such as "application location of the detection control area" and "object of the acquired data", the division of warning levels can be further refined. For example, when the data feature of the absolute value of the numerical difference is "detection control area of ​​entrances and exits", it indicates that special attention should be paid to the safety status of the area, because entrances and exits are often places with higher safety risks. If the data object is "operating parameters of key equipment", it indicates that slight changes in these parameters may have a significant impact on the stable operation of the entire system. By associating these data features with the warning plan level, it can be ensured that when an abnormality occurs, the root cause of the problem can be quickly identified.

[0131] Step S440: assigning weights based on the level of the early warning scheme to the data items in the monitoring device that correspond to the data features.

[0132] It should be clear that step S440 is to ensure that when a security warning is issued, the corresponding degree of attention can be given according to different data characteristics and their impact on the overall security situation.

[0133] Specifically, by assigning weights to each data item, the early warning system can pay more attention to 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 key equipment, then this data item may be assigned a higher weight, because slight changes 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 changes in these parameters have relatively little impact on overall safety.

[0134] It should be further explained that, in actual operation, the function of assigning weights is realized through algorithms and programs in computers in the prior art, which ensures the flexibility and adaptability of the early 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, so as to further optimize the warning strategy and improve the accuracy of the warning system.

[0137] Specifically, step S500 can discover redundancy or deficiencies in the warning strategy by comparing the difference data between different security warning schemes. For example, if two warning schemes target similar monitoring data and thresholds, but there are large differences in 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, step S500 is further divided into steps S510 to S530.

[0139] Step S510: Based on the type of the security warning scheme, fill the data value and type in the security 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.

[0141] It should be added that the meaning of step S510 is to organize and classify the key data values ​​in each safety warning scheme according to their types to facilitate horizontal and vertical comparisons. In this way, the differences between different warning schemes can be clearly displayed, thereby providing intuitive data support for subsequent optimization.

[0142] Specifically, in step S510, it is first necessary to determine the structure of the difference comparison table to ensure that each row and each column corresponds to a specific type of safety warning plan. Then, the key data values ​​in each plan, such as the absolute value of the numerical difference, accuracy index and data characteristics, are filled in 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 is possible to quickly identify which warning plans have large differences in certain aspects, thereby providing a basis for subsequent analysis and optimization.

[0143] Step S520: Calculate the differences between the data values ​​of corresponding rows and columns in 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 difference between the data values ​​of corresponding rows and columns in the difference comparison table, so as to further intuitively show the degree of difference between different safety warning schemes.

[0145] Specifically, step S520 involves comparing the key data values ​​of each early warning scheme one by one, calculating the absolute value of their numerical differences, precision indicators, and differences between data features. For example, for two early warning schemes, their differences in the absolute value of their numerical differences, as well as the differences in precision indicators and data features can be calculated. In this way, a difference data matrix can be generated, in which each element represents the degree of difference between the two early warning schemes in a specific data feature.

[0146] In addition, during the specific implementation of step S520, in order to ensure the accuracy and practicality of the difference data matrix, a variety of mathematical methods can be used for calculation, such as any one or two of the Euclidean distance, Manhattan distance or cosine similarity. It should be noted that these methods can quantify the differences between early warning schemes from different angles, thereby providing a more scientific basis for subsequent analysis and optimization.

[0147] Step S530: The matrix data in the difference data matrix that matches the type of the safety warning scheme is the difference data.

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

[0149] Step S600: adjusting the control authority of the monitoring device within the detection control area based on the difference 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 and control area according to the difference data type and value to ensure the efficiency and accuracy of the early warning system. Specifically, by analyzing the difference 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 and degree of association of the monitoring device with key equipment. For example, if a monitoring device has a large difference value in key data characteristics, it indicates that it has a high importance in the early warning process. Therefore, the control authority of the device can be appropriately increased to enable it to collect and transmit data more frequently, thereby improving the response speed and accuracy of the early warning system.

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

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

[0154] Step S610: Associating the difference 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 type with the specific type of detection control area in order to more accurately 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 response, while a low-risk area may not require such a high monitoring frequency.

[0156] In step S610, the detection and control areas need to be classified first to determine the type and characteristics of each area. Then, the difference data types obtained in step S530 are matched 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, special attention may be paid to difference data types related to chemical leaks, while for a low-risk area in a residential area, more attention may be paid to difference data types related to noise or air quality.

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

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

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

[0160] In step S620, the search program in the computer first sorts the values ​​in the difference data matrix in step S520 to find the difference data with larger values, and then determines the priority of management authority adjustment based on the values ​​of these difference data. The larger the value of the difference data, the higher the priority of the corresponding early warning plan adjustment.

[0161] For example: If a warning scheme has a large difference in the absolute value of the numerical difference, it indicates that there may be major problems in the accuracy of the warning. Therefore, it is necessary to adjust its control authority as a priority to ensure the accuracy and timeliness of the warning system. If a warning scheme has a large difference in accuracy indicators, it may be necessary to adjust its data processing algorithm as a priority to improve the accuracy of the warning scheme.

[0162] In order to ensure the scientificity and rationality of the adjustment of management authority, step S620 can also adopt a variety of mathematical methods to assist decision-making, such as the analytic hierarchy process (AHP) or the fuzzy comprehensive evaluation method. These methods can comprehensively consider the numerical size of the difference data and other relevant factors of the early warning plan, so as to determine a more reasonable management authority adjustment priority.

[0163] Step S630: The management authority is divided into multiple levels, for example, high, medium, and low, each level corresponding to different monitoring device control authority.

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

[0165] In step S630, it is first necessary to define the specific meaning of each authority level and the corresponding control authority. 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. A medium authority level may be between high and low authorities, suitable for general risk situations. A low authority level means that the operating frequency and data processing volume of the monitoring device are relatively low, suitable for low-risk periods.

[0166] Step S640: Determine the monitoring device whose permissions need to be adjusted based on the priority and the value, and generate a corresponding permission adjustment plan.

[0167] It should be clear that the meaning of step S640 is to determine the monitoring devices that need to adjust permissions based on the priority and numerical value, and generate a corresponding permission adjustment plan. Specifically, the computer can identify which monitoring devices need to have their control permissions adjusted first during the early warning process by analyzing the management permission adjustment priority and the numerical value of the difference data determined in step S620. For example: if a monitoring device has a large difference value in key data features, it indicates that it is of high importance in the early warning process. Therefore, the control authority of the device can be appropriately increased so that it can collect and transmit data 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 priority and numerical value, and then generates corresponding permission adjustment plans. These plans will specify in detail which monitoring devices need to increase or decrease control permissions, as well as the specific operating frequency and data processing volume after adjustment. For example: for high-priority monitoring devices, it may be necessary to set their control permissions to "high" to ensure that they can perform more frequent data collection and transmission; while for low-priority monitoring devices, their control permissions may be set to "low" to reduce resource consumption and data processing burden.

[0169] Step S650: Send the permission adjustment plan to the monitoring device in 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 device within the detection and control area to facilitate real-time updating and optimization of the operating status of the early warning system. Specifically, the permission adjustment plan generated by step S640 is transmitted to each monitoring device through the permission adjustment module in the computer system to ensure that each device can operate according to the latest permission adjustment plan.

[0171] It should be noted that to ensure security, during the actual implementation of step S650, the permission adjustment module first verifies the identity and authority of the monitoring device 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 corresponding adjustments to ensure that its operating status is consistent with the requirements of the early warning system.

[0172] For example: If the control authority of a 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 authority of a monitoring device is adjusted to "low", its data collection and transmission frequency will be reduced accordingly to reduce resource consumption and data processing burden.

[0173] In addition, in order to ensure the smooth implementation of the authority adjustment plan, step S650 can also be assisted by a variety of technical means, such as using wireless communication technology, wired network or satellite communication, through the use of wireless communication technology, wired network or satellite communication to adapt to the communication needs and environmental conditions of different detection and control areas.

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

[0175] like Figure 3As shown, the present application introduces a smart construction site safety control system based on sensor monitoring, which adopts the above-mentioned method for comprehensive environment and safety management of smart construction sites and integrates multiple key components.

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

[0177] Secondly, the system is equipped with a data acquisition and control module, whose main task is to acquire 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, so that managers can understand the environment 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 different levels of safety warning plans based on real-time monitoring data and preset thresholds, so that managers can take corresponding measures according to the severity of the warning.

[0180] In order to optimize the system operation, the system also has a difference data analysis module, which is responsible for obtaining the difference data between different safety warning schemes, analyzing and processing them, and then adjusting the control authority of the monitoring device in the detection and control area to improve the overall operation efficiency of the system.

[0181] In order to enable the monitoring device to operate under the best authority, the system also includes a permission adjustment module, which adjusts the control authority of the monitoring device according to the type and value of the difference data to ensure the accuracy and reliability of the monitoring data.

[0182] In order 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 collection frequency of monitoring data, and uses confidence judgment rules to correct real-time data to ensure that the acquired 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 promptly understand the safety status of the construction site and the system operation status and take corresponding measures. This not only improves the safety of the construction site, but also improves management efficiency.

[0184] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments 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 devices within the detection and control area based on the difference in data types and values.

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 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; The data values ​​of the interaction points between the straight line and the fitted curve are obtained, and multiple preset thresholds are generated based on the type of the fitted curve.

4. According to claim 3, 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.

5. 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.

6. The method for intelligent construction site safety control based on sensor monitoring according to claim 1 is characterized in that: 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.

7. The method for intelligent construction site safety control based on sensor monitoring according to claim 1 is characterized in that: 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.

8. The method for intelligent construction site safety control based on sensor monitoring according to claim 1 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.

9. 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 8, 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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