Intelligent supervision and monitoring data analysis method

Abstract

The present application is applicable to the field of supervision and on-site monitoring technology, and in particular, it relates to an intelligent supervision and on-site monitoring data analysis method, which includes: acquiring construction site images; analyzing the construction site images to obtain construction feature information; analyzing the construction feature information to obtain mapping feature information; analyzing the mapping feature information to determine the matching results; and obtaining the event response level based on the matching results. The method can realize automatic classification of key links in the construction process, which is convenient for quickly locating and identifying the focus points in the construction; it can objectively evaluate the compliance and safety of the construction process and reduce the errors caused by subjective judgment; based on the matching results, it can automatically give the event response level to provide timely and accurate decision support, which is helpful to take timely measures to deal with problems in the construction, thereby improving the efficiency, accuracy and safety of the supervision work.

Description

Technical Field

[0001] The present application belongs to the technical field of on-site supervision, and in particular, relates to an intelligent on-site supervision data analysis method. Background Art

[0002] With the rapid development of infrastructure construction, especially in large-scale engineering projects such as roads, bridges, and tunnels, supervision work has become more and more complex and important. Traditional supervision methods usually rely on manual on-site observation and recording. In recent years, more and more technologies have been introduced into supervision work, such as video surveillance, IoT sensors, and artificial intelligence, to replace manual monitoring.

[0003] However, most of the existing intelligent supervision technologies are still at the simple video monitoring level, lacking in-depth understanding of the construction process and intelligent analysis capabilities. Summary of the invention

[0004] The embodiment of the present application provides a method for analyzing data of an intelligent on-site supervision, which can solve the problem that most of the existing intelligent supervision technologies still remain at the simple video monitoring level and lack an in-depth understanding of the construction process and intelligent analysis capabilities.

[0005] In a first aspect, an embodiment of the present application provides a method for analyzing data of an intelligent monitoring station, including:

[0006] Acquire a construction site image; wherein the construction site image is used to reflect the real-time picture of the project;

[0007] Analyze the construction site image to obtain construction feature information; wherein the construction feature information includes feature data of multiple focus points of the construction project;

[0008] Analyze the construction feature information to obtain mapping feature information; the mapping feature information is used to represent the classification data corresponding to each of the focus points of the construction feature information;

[0009] Analyze the mapping feature information to determine a matching result; wherein the matching result is used to indicate the matching degree between the mapping feature information and the preset focus point;

[0010] An event response level is obtained based on the matching result.

[0011] The above technical solutions in the embodiments of the present application have at least the following technical effects:

[0012] The intelligent supervision on-site data analysis method provided by the present application obtains the construction site image; analyzes the construction site image to obtain the construction feature information including the feature data of multiple focus points of the construction project; then analyzes the construction feature information to obtain the mapping feature information of the classification data corresponding to each focus point for representing the construction feature information; analyzes the mapping feature information to determine the matching result for representing the matching degree between the mapping feature information and the preset focus point; and obtains the event response level based on the matching result. The method can realize automatic classification of the key links in the construction process by converting the construction feature information into mapping feature information, so as to facilitate the rapid positioning and identification of the focus points in the construction; by analyzing the matching degree between the mapping feature information and the preset focus points, the compliance and safety of the construction process can be objectively evaluated, and the error caused by subjective judgment can be reduced; based on the matching result, the event response level can be automatically given to provide timely and accurate decision support, which is helpful to take timely measures to deal with problems in the construction, thereby improving the efficiency, accuracy and safety of the supervision work.

[0013] In a possible implementation manner of the first aspect, the analyzing according to the construction feature information to obtain mapping feature information includes:

[0014] Analyze the construction feature information to obtain construction category information; wherein the construction category information is used to reflect the sub-projects of the construction project;

[0015] Analyze the construction category information to obtain sub-project information; wherein the sub-project information is used to reflect multiple sub-projects in the sub-project;

[0016] The sub-project information is mapped to a multi-dimensional space to obtain mapping feature information; wherein each dimension of the multi-dimensional space corresponds to one of the focus points in the sub-project information.

[0017] In a possible implementation manner of the first aspect, the analyzing according to the construction feature information to obtain the construction category information includes:

[0018] Analyze the construction feature information to obtain key feature points;

[0019] The construction category information is obtained based on the key feature points.

[0020] In a possible implementation manner of the first aspect, analyzing according to the construction feature information to obtain key feature points includes:

[0021] Analyze the construction feature information to obtain feature values ​​of each of the focus points;

[0022] Comparing the feature value with a preset threshold, screening out the feature value greater than the preset threshold, and obtaining a first feature group;

[0023] An analysis is performed based on the first feature group to obtain key feature points.

[0024] In a possible implementation manner of the first aspect, the analyzing according to the first feature group to obtain key feature points includes:

[0025] Associating any two of the focus points corresponding to the feature values ​​in the first feature group to obtain multiple feature groups;

[0026] Acquire the association value of the feature group; wherein the association value is used to reflect the degree of association between the two focus points;

[0027] Key feature points are obtained based on the association values.

[0028] In a possible implementation manner of the first aspect, obtaining a key feature point based on the association value includes:

[0029] Matching the association value with a preset association value, and determining the feature group matching the preset association value as a key feature group;

[0030] The feature values ​​of the focus points in the key feature group are compared, and the focus point with the larger feature value is determined as a key feature point.

[0031] In a possible implementation manner of the first aspect, the analyzing according to the mapping feature information to determine the matching result includes:

[0032] Acquire mapping data in the mapping feature information; wherein the mapping data is used to indicate data of each of the focus points mapped to a multi-dimensional space;

[0033] Comparing the mapping data with standard mapping data to obtain a matching score;

[0034] A matching result is determined according to the matching score.

[0035] In a possible implementation manner of the first aspect, comparing the mapping data with standard mapping data to obtain a matching score includes:

[0036] Comparing the mapping data with the standard mapping data to obtain a difference value; wherein the difference value is used to reflect the distance between the mapping data and the standard mapping data in the multi-dimensional space;

[0037] A matching score is obtained based on the difference value.

[0038] In a possible implementation manner of the first aspect, determining a matching result according to the matching score includes:

[0039] Matching the matching score with a preset score range to obtain a matching level;

[0040] A corresponding matching result is obtained based on the matching level.

[0041] In a possible implementation manner of the first aspect, obtaining the event response level based on the matching result includes:

[0042] When the matching result is lower than the first preset threshold, the event response level is high; wherein the measures corresponding to the high level include starting an emergency plan and sending a scheduling resource signal;

[0043] And / or, obtaining the event response level based on the matching result further includes:

[0044] When the matching result is lower than a second preset threshold, obtaining the number of construction workers;

[0045] When the number of construction workers exceeds the preset number, the incident response level is intermediate; the corresponding measures for the intermediate level include sending a phone call or on-site broadcast alarm system instructions to correct or suspend the work;

[0046] When the number of construction workers is lower than the preset number, the event response level is low; wherein the measures corresponding to the low level include sending an early warning signal.

[0047] In a second aspect, an embodiment of the present application provides an intelligent supervision on-site data analysis system, including:

[0048] An acquisition module is used to acquire a construction site image; wherein the construction site image is used to reflect a real-time picture of the project;

[0049] A first analysis module is used to analyze the construction site image to obtain construction feature information; wherein the construction feature information includes feature data of multiple focus points of the construction project;

[0050] A second analysis module is used to analyze the construction feature information to obtain mapping feature information; the mapping feature information is used to represent the classification data corresponding to each of the focus points of the construction feature information;

[0051] A third analysis module, used to analyze the mapping feature information and determine a matching result; wherein the matching result is used to indicate the matching degree between the mapping feature information and the preset focus point;

[0052] A determination module is used to obtain an event response level based on the matching result.

[0053] In a third aspect, an embodiment of the present application provides an intelligent supervision on-site data analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described in the first aspect when executing the computer program.

[0054] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above-mentioned first aspects is implemented.

[0055] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an intelligent supervision on-site data analysis device, the intelligent supervision on-site data analysis device executes the intelligent supervision on-site data analysis method described in any one of the above-mentioned first aspects.

[0056] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 It is a flow chart of the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0059] Figure 2 It is a schematic diagram of the implementation process of step S300 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0060] Figure 3 It is a schematic diagram of the implementation process of step S310 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0061] Figure 4 It is a schematic diagram of the implementation flow of step S311 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0062] Figure 5 It is a schematic diagram of the implementation flow of step S3113 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0063] Figure 6It is a schematic diagram of the implementation flow of step S31133 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0064] Figure 7 It is a schematic diagram of the implementation process of step S400 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0065] Figure 8 It is a schematic diagram of the implementation process of step S420 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0066] Fig. 9 It is a schematic diagram of the implementation process of step S430 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0067] Fig.10 It is a schematic diagram of the implementation process of step S500 in the intelligent monitoring station data analysis method provided in the embodiment of the present application;

[0068] Fig.11 It is a structural diagram of the intelligent supervision station data analysis system provided in the embodiment of the present application;

[0069] Fig.12 It is a structural diagram of the intelligent supervision on-site data analysis equipment provided in the embodiment of the present application. DETAILED DESCRIPTION

[0070] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0071] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0072] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0073] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0074] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0075] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0076] With the rapid development of infrastructure construction, especially in large-scale engineering projects such as roads, bridges, and tunnels, supervision work has become more and more complex and important. Traditional supervision methods usually rely on manual on-site observation and recording. In recent years, more and more technologies have been introduced into supervision work, such as video surveillance, IoT sensors, and artificial intelligence, to replace manual monitoring.

[0077] However, most of the existing intelligent supervision technologies are still at the simple video monitoring level, lacking in-depth understanding of the construction process and intelligent analysis capabilities.

[0078] To solve the above problems, the embodiment of the present application provides an intelligent supervision on-site data analysis method. In the method, by acquiring a construction site image; analyzing the construction site image, obtaining construction feature information including feature data of multiple focus points of the construction project; then analyzing the construction feature information, obtaining mapping feature information of classification data corresponding to each focus point for representing the construction feature information; analyzing the mapping feature information, determining a matching result for representing the matching degree between the mapping feature information and the preset focus point; and obtaining an event response level based on the matching result. By converting the construction feature information into mapping feature information, the method can realize automatic classification of key links in the construction process, facilitate rapid positioning and identification of focus points in the construction; by analyzing the matching degree between the mapping feature information and the preset focus points, the compliance and safety of the construction process can be objectively evaluated, and the error caused by subjective judgment can be reduced; based on the matching result, the event response level can be automatically given to provide timely and accurate decision support, which is helpful to take timely measures to deal with problems in the construction, thereby improving the efficiency, accuracy and safety of supervision work.

[0079] The intelligent supervision on-site data analysis method provided in the embodiment of the present application can be applied to an intelligent supervision on-site data analysis device. At this time, the intelligent supervision on-site data analysis device is the executor of the intelligent supervision on-site data analysis method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the intelligent supervision on-site data analysis device.

[0080] For example, the intelligent supervision station data analysis device can be on terminal devices such as mobile phones, tablets, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), desktop computers, smart large screens, smart TVs, etc., handheld devices with wireless communication functions, computing devices or other processing devices connected to wireless modems, Internet of Things terminals, computers, laptops, handheld communication devices, handheld computing devices, satellite wireless devices, wireless modem cards, TV set top boxes (STB), customer premises equipment (CPE) and / or other devices used to communicate on wireless systems and next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved public land mobile networks (PLMN).

[0081] In order to better understand the intelligent supervision on-site data analysis method provided in the embodiment of the present application, the specific implementation process of the intelligent supervision on-site data analysis method provided in the embodiment of the present application is exemplarily introduced below.

[0082] Figure 1 A schematic flow chart of an intelligent supervision on-site data analysis method provided in an embodiment of the present application is shown, and the intelligent supervision on-site data analysis method includes:

[0083] S100, obtaining a construction site image; wherein the construction site image is used to reflect the real-time picture of the project.

[0084] It is understood that real-time images of the construction site can be obtained through cameras or other image acquisition devices. The acquisition form can be through installing cameras at each construction site, supervisors wearing mobile recorders, drone photography, etc., but is not limited to these.

[0085] S200, analyzing the construction site image to obtain construction feature information; wherein the construction feature information includes feature data of multiple focus points of the construction project.

[0086] It can be understood that after acquiring the image of the construction site, the construction feature information is extracted through image processing algorithms and analysis tools. These feature information include specific data of various points of concern in the construction project, such as the location of construction equipment, the activities of construction personnel (such as safety conditions, personnel distribution, etc.), the stacking location of construction materials, construction progress, etc. These feature data provide basic data support for subsequent analysis. Through image recognition technology, such as target detection and segmentation, various construction elements in the image can be identified and marked. For example, object detection algorithms can be used to identify specific objects in the image (such as safety helmets, construction machinery, etc.) and calculate their number and location; semantic segmentation technology can be used to distinguish different areas (such as work areas, rest areas, etc.); and deep learning models can also be used to identify the status of construction activities (such as whether construction is carried out in the prescribed order). Furthermore, data extraction can also pre-process the image, such as denoising and contrast enhancement, to improve the accuracy of feature recognition.

[0087] S300, analyzing the construction feature information to obtain mapping feature information; the mapping feature information is used to represent classification data corresponding to each focus point of the construction feature information.

[0088] It can be understood that these classified data can help understand the various dynamics and states of the construction process. By classifying and mapping the feature data, construction information can be processed and analyzed more systematically. Different construction projects or activities can be mapped to different categories or states by clustering or classifying the construction feature information. This process helps identify abnormal situations and potential problems in the construction process.

[0089] As an optional embodiment of this application, please refer to Figure 2 In step S300, the construction feature information is analyzed to obtain mapping feature information, including:

[0090] S310, analyzing the construction characteristic information to obtain construction category information; wherein the construction category information is used to reflect the sub-projects of the construction project.

[0091] It can be understood that the sub-projects may include earthwork, pavement, foundation and substructure, prefabrication and installation of superstructure, on-site casting of superstructure, bridge deck system and auxiliary projects, and other decoration projects (such as tunnel support lining, guardrail disassembly and assembly, sign installation, etc.). The construction category information can be obtained by matching the construction feature information with the data in the construction database, or by inputting the construction feature information into the learning model to obtain the corresponding construction category information, etc., but not limited to this. The construction database refers to a database containing all construction feature information and corresponding construction category information. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data is sorted, classified and archived, useful information and rules are extracted, and the relevant data is saved in the database to form a construction database. The learning model is trained with multiple sets of training data, and each set of training data in the multiple sets of training data includes construction feature information and corresponding construction category information.

[0092] In one possible implementation, see Figure 3 In step S310, the construction feature information is analyzed to obtain construction category information, including:

[0093] S311, analyzing the construction feature information to obtain key feature points.

[0094] It can be understood that key feature points refer to processes or procedures that are relatively unique during the construction process and are used to distinguish sub-projects. Key feature points can be obtained by matching construction feature information with data in the construction database, or by inputting construction feature information into the learning model to obtain corresponding key feature points, etc., but not limited to this. Construction database refers to a database containing all construction feature information and corresponding key feature points. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data is sorted, classified and archived, useful information and rules are extracted, and the relevant data is saved in the database to form a construction database. The learning model is trained with multiple sets of training data, and each set of training data in the multiple sets of training data includes construction feature information and corresponding key feature points.

[0095] In one possible implementation, see Figure 4 In step S311, the construction feature information is analyzed to obtain key feature points, including:

[0096] S3111, analyzing the construction characteristic information to obtain characteristic values ​​of each focus point.

[0097] It can be understood that the characteristic value is the quantitative data of the focus point, which can be parameters such as size, position, state, etc. Different focus points may have different characteristic values, which can reflect their importance or abnormality in construction. They can be set manually or obtained from the construction database, etc., but are not limited to this.

[0098] S3112, comparing the characteristic value with a preset threshold, screening out characteristic values ​​greater than the preset threshold, and obtaining a first characteristic group.

[0099] It can be understood that the preset threshold is a pre-set value, which can be set by means of past experience, industry standards, laboratory experiments, etc., but is not limited thereto. The first feature group includes at least one feature value.

[0100] S3113, analyzing the first feature group to obtain key feature points.

[0101] With such a setting, by quantifying the degree of particularity of the focus points, focus points with a higher degree of particularity can be efficiently screened out; through the screened out first feature group, the determination range of key feature points can be further narrowed, thereby improving the accuracy of identifying key feature points.

[0102] In one possible implementation, see Figure 5 In step S3113, the key feature points are obtained by analyzing the first feature group, including:

[0103] S31131, associate any two of the focus points corresponding to the feature values ​​in the first feature group to obtain multiple feature groups.

[0104] It is understandable that in a construction project, the particularity of a single focus point may not be directly used to determine the classification of the project. Only when other focus points that match the focus point appear can they be used to directly determine the classification of the project. By means of pairwise association, a relationship network between different focus points is constructed to identify which focus points may jointly affect a specific aspect of the construction process. Each feature value corresponds to a focus point. We can pair these focus points in pairs through algorithms or programming logic to generate different feature groups. For example, if there are five focus points A, B, C, D, and E, then 10 feature groups can be formed, namely AB, AC, AD, AE, BC, BD, BE, CD, CE, and DE. By means of pairwise association, the potential correlation between different focus points can be revealed, laying the foundation for further analysis.

[0105] S31132, obtaining a correlation value of the feature group; wherein the correlation value is used to reflect the degree of correlation between two focus points.

[0106] It can be understood that there is an association value between two different focus points. The association value can be obtained through a construction database, or through a learning model, etc., but is not limited thereto. By quantifying the degree of association, it can be clear which focus points have a closer relationship, which is helpful for determining key feature points in subsequent steps.

[0107] S31133, obtain key feature points based on the association value.

[0108] It can be understood that the correlation values ​​of all feature groups can be sorted, a ranking can be given, the top N feature groups with the highest rankings can be selected as key feature groups, and the focus points with the largest feature values ​​can be selected from them; a series of threshold intervals of correlation values ​​can also be defined, the feature groups can be classified into different intervals according to the correlation values, and those feature groups whose correlation values ​​fall in higher threshold intervals can be selected as key feature groups, thereby obtaining key feature points, and so on, but not limited to these.

[0109] In one possible implementation, see Figure 6 In step S31133, key feature points are obtained based on the association value, including:

[0110] S311331, matching the associated value with a preset associated value, and determining the feature group matching the preset associated value as the key feature group.

[0111] It can be understood that there is a preset correlation value between the more special focus points of different projects and other focus points. This preset correlation value can be considered to be manually input, or obtained through a learning model, etc., but is not limited to this. The learning model is trained through multiple sets of training data, and each set of training data in the multiple sets of training data includes two different focus points and corresponding correlation values. When the correlation value matches the preset correlation value, it means that there is a focus point in the feature group for identifying the project classification.

[0112] S311332, comparing the feature values ​​of the focus points in the key feature group, and determining the focus point with a larger feature value as the key feature point.

[0113] It is understandable that the key feature point can be obtained by directly comparing the eigenvalues ​​of the focus points in the key feature group, or the eigenvalues ​​can be sorted and the focus point with the largest eigenvalue can be selected as the key feature point, etc., but it is not limited thereto.

[0114] This setting, through clear steps and standards, provides a basis for determining key feature points, reduces the uncertainty caused by subjective judgment, and enhances the reliability of decision-making. Secondly, it enhances the ability to identify and manage risks. By matching the associated value with the preset associated value, it can quickly identify those focus combinations with high correlation in the construction process. These combinations are often potential risk points, and then these focus points can be monitored and managed in a targeted manner, effectively reducing the risks in the construction process.

[0115] S312, obtaining construction category information based on key feature points.

[0116] It can be understood that different construction category information corresponds to one or more key feature points. The key feature points can be input into the construction database for matching to obtain the corresponding construction category information, or the key feature points can be input into the learning model, and the learning model then outputs the construction category information, etc., but not limited to this.

[0117] In this way, by identifying key feature points, the specific items of the construction project can be more accurately evaluated and identified, thereby enabling the specific activities of the construction project to be quickly identified.

[0118] S320, analyzing the construction category information to obtain sub-project information; wherein the sub-project information is used to reflect multiple sub-projects in the sub-project.

[0119] It can be understood that after determining the construction category information, the various sub-projects included in the project can be identified from sources such as construction plans, design drawings, and contract documents; sub-project information can also be obtained in the construction database, etc., but not limited to this. The construction database contains different construction category information and corresponding sub-project information. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data will be sorted, classified, and archived, useful information and rules will be extracted, and the relevant data will be saved in the database to form a construction database.

[0120] S330, mapping the sub-project information to a multi-dimensional space to obtain mapping feature information; wherein each dimension of the multi-dimensional space corresponds to a focus point in the sub-project information.

[0121] It can be understood that each dimension in the multidimensional space represents a focus point in the sub-project information, and each parameter in the sub-project information can be converted into a coordinate point or vector in the space. In order to map these data to the multidimensional space, mapping rules need to be defined. These rules should be able to convert the data into a value between 0 and 1 so that it can be represented in the multidimensional space. Exemplarily, it is assumed that each sub-project includes: progress, quality, and safety. The mapping rule is progress = completed time ÷ total construction period, quality = quality assessment score ÷ full score, and safety = safety management score ÷ full score. In this way, the value of each focus point in each dimension can be calculated. Assuming that the calculated progress is 0.67, the quality is 0.85, and the safety is 0.9, the corresponding coordinate points obtained according to the mapping results are (0.67, 0.85, 0.9), and so on.

[0122] S400, analyzing the mapping feature information to determine a matching result; wherein the matching result is used to indicate the matching degree between the mapping feature information and the preset focus point.

[0123] It can be understood that the higher the matching degree, the more the various focus points of the project meet the expected or specified operation. The distance between each focus point in the mapping feature information and the standard data point can be calculated, and the calculated distance value can be converted into a matching score. Then, based on the calculated matching score, it is compared with the preset score range to obtain the final matching result; the mapping feature information can also be input into the learning model to obtain the corresponding matching result, etc., but not limited to this. The learning model is trained with a large amount of training data of mapping feature information and corresponding matching results.

[0124] In one possible implementation, see Figure 7 In step S400, the matching result is determined based on the analysis of the mapping feature information, including:

[0125] S410, obtaining mapping data in the mapping feature information; wherein the mapping data is used to indicate data of each focus point mapped to a multi-dimensional space.

[0126] It can be understood that the mapping data is the coordinates of each focus point in the multidimensional space. Taking one of the focus points as an example, assuming that its "progress = 0.8", "quality = 0.85", and "safety = 0.9", the data point of this focus point can be expressed as (0.8, 0.85, 0.9); if "progress = 0.7", "quality = 0.95", and "safety = 1.0", the data point of this focus point can be expressed as (0.7, 0.95, 1.0), and so on.

[0127] S420, comparing the mapping data with the standard mapping data to obtain a matching score.

[0128] It can be understood that the standard mapping data is the expected position or coordinate of each focus point in the multi-dimensional space, which can be set manually or obtained from the construction database, etc., but not limited thereto. By comparing or calculating the actual mapping coordinate point of each focus point in the mapping data with the standard mapping coordinate point of each focus point corresponding to the standard mapping data, a matching score can be obtained.

[0129] In one possible implementation, see Figure 8 In step S420, the mapping data is compared with the standard mapping data to obtain a matching score, including:

[0130] S421, comparing the mapping data with the standard mapping data to obtain a difference value; wherein the difference value is used to reflect the distance between the mapping data and the standard mapping data in the multi-dimensional space.

[0131] It can be understood that the difference value can be obtained by the Euclidean distance algorithm. For example, in a construction project, the standard data points of a certain point of interest are "progress = 1.0", "quality = 0.9", and "safety = 0.95". These data points can be represented as (1.0, 0.9, 0.95) in three-dimensional space. If the mapped data points of the point of interest are "progress = 0.8", "quality = 0.85", and "safety = 0.9", these data points can be represented as (0.8, 0.85, 0.9) in three-dimensional space. The Euclidean distance formula is used to calculate the distance between the actual data point and the standard data point. The calculation formula is, where (x1, y1, z1) is the coordinate of the mapped data point, and (x2, y2, z2) is the coordinate of the standard data point, that is, the distance between (0.8, 0.85, 0.9) and (1.0, 0.9, 0.95) is calculated to obtain the difference value.

[0132] S422, obtaining a matching score based on the difference value.

[0133] It is understood that the smaller the difference value, the higher the matching score. Different difference values ​​can be defined to correspond to a matching score, or different interval distributions of difference values ​​can be defined to correspond to a matching score, and then the calculated difference value is matched with the preset corresponding relationship to obtain the corresponding matching score; the calculated difference value can also be converted into a matching score, for example, a normalization method can be used, that is, matching score = 1-d ÷ dmax, where dmax is the possible maximum distance value, etc., but not limited thereto.

[0134] S430: Determine a matching result according to the matching score.

[0135] It is understandable that the calculated matching scores of each focus point can be compared with a preset score range to obtain a final matching result; the matching scores of each focus point can also be weighted to obtain a final matching result, etc., but not limited thereto.

[0136] In one possible implementation, see Fig. 9 In step S430, a matching result is determined according to the matching score, including:

[0137] S431, matching the matching score with a preset score range to obtain a matching level.

[0138] It is understood that the preset score interval can be set according to historical data, industry standards or expert experience, can be obtained from the construction database, etc., but is not limited to this. For example, the preset score interval can be divided into the following levels: 0.9 and above is excellent, 0.7 to 0.9 is good, 0.5 to 0.7 is medium, and below 0.5 is poor. By comparing the matching score with these score intervals, the overall status of the construction project can be determined. For example, if the matching score of the focus point is 0.65, it will be rated as "medium" status, and so on.

[0139] S432: Obtain corresponding matching results based on the matching level.

[0140] It is understood that the matching results will reflect the overall health of the construction project and provide a basis for subsequent response measures. The higher the matching level, the more consistent the matching results are with expectations, and the lower the matching level, the more the matching results deviate from expectations. For example, if the matching level is "poor", it may indicate that the construction project is seriously deviating from the standard and immediate action is required.

[0141] S500: Obtain an event response level based on the matching result.

[0142] It can be understood that different matching results correspond to an event response level. The more the matching result conforms to the expected standard, the lower the event response level. The more the matching result deviates from the expected standard, the higher the event response level.

[0143] In one possible implementation, see Fig.10 In step S500, the event response level is obtained based on the matching result, including:

[0144] S510a, when the matching result is lower than the first preset threshold, the event response level is high; wherein the measures corresponding to the high level include starting an emergency plan and sending a scheduling resource signal.

[0145] It can be understood that the first preset threshold is a pre-set value, which can be manually input by humans, or obtained from the construction database, etc., but is not limited to this. The matching result can be first converted into a compliance value through a learning model or through a certain function conversion. The more the matching result conforms to expectations, the greater the compliance value. When the matching result is lower than the first preset threshold, it means that the focus has seriously deviated from the expected construction standard, seriously affecting factors such as construction quality and safety. For example, if the matching score range is 0 to 1 (where 1 means full compliance with expectations), then a simple conversion can be to directly use the matching score as the compliance value. If the matching score is 0.6, then the compliance value is also 0.6. If the matching score is lower than the first preset threshold (for example, 0.7), the matching result is considered to be poor, triggering advanced response measures.

[0146] In one possible implementation, see Fig.10 In step S500, the event response level is obtained based on the matching result, and further includes:

[0147] S510b: When the matching result is lower than the second preset threshold, the number of construction workers is obtained.

[0148] It can be understood that the second preset threshold is a pre-set value, which can be manually input by humans, or obtained from the construction database, etc., but is not limited to this. The second preset threshold is greater than the first preset threshold. The number of construction personnel is the number of people related to the point of interest. The corresponding number of construction personnel can be obtained by inputting the point of interest into the construction database for matching, or the number of construction personnel on site can be obtained through imaging devices such as cameras, etc., but is not limited to this. When the matching result is higher than the first preset threshold but lower than the second preset threshold, it means that the point of interest may deviate from the expected construction standard and may affect factors such as construction quality and safety. At this time, it is necessary to obtain the number of construction personnel at the current construction site in order to evaluate the management and control capabilities of the site. The acquisition of the number of construction personnel helps to understand the actual working conditions on site and can provide data support for the formulation of further response measures.

[0149] S511b, when the number of construction workers exceeds the preset number, the event response level is intermediate; the corresponding measures for the intermediate level include sending a phone call or on-site broadcast alarm system instructions to correct or suspend the work.

[0150] It is understandable that if the number of construction workers is higher than the preset number, it means that the scale of on-site operations is large, which may lead to increased management complexity. At this time, the incident response level is rated as intermediate. Intermediate response measures usually include sending a phone call or on-site broadcast alarm system instructions, requiring the on-site person in charge to take immediate measures to correct or suspend operations. These measures are aimed at timely controlling the status of the construction site and avoiding the expansion of potential risks. It may also include safety training for construction workers and strengthening on-site supervision.

[0151] S511B, when the number of construction workers is lower than the preset number, the event response level is low; the corresponding measures for the low level include sending an early warning signal.

[0152] It is understandable that when the number of construction personnel is lower than the preset number, it means that the scale of the work on site is small and the risk is relatively low. At this time, the incident response level is rated as low. Low-level response measures include sending early warning signals to remind relevant personnel to pay attention to the construction situation and make necessary adjustments. These early warning signals may be sent via email, SMS or automatic alarm system, with the purpose of warning potential problems in advance so that intervention can be made before the problem becomes a real risk.

[0153] With this setting, by determining the event response level based on the matching results, effective supervision of the construction process can be achieved, problems can be discovered and solved in a timely manner, and construction quality and safety can be ensured. This method not only improves the level of intelligent construction management, but also significantly reduces the risks in the construction process, protects the life and property safety of construction workers, and also improves the overall efficiency and quality of the construction project, providing intelligent management for the supervision process.

[0154] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0155] Corresponding to the intelligent supervision on-site data analysis method described in the above embodiment, the embodiment of the present application also provides an intelligent supervision on-site data analysis system, and each module of the system can implement each step of the intelligent supervision on-site data analysis method. Fig.11 A structural block diagram of the intelligent supervision on-site data analysis method provided in an embodiment of the present application is shown. For the sake of convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0156] Reference Fig.11 , the intelligent supervision station data analysis system includes:

[0157] The acquisition module is used to acquire construction site images; wherein the construction site images are used to reflect the real-time picture of the project.

[0158] The first analysis module is used to analyze the construction site image to obtain construction feature information; wherein the construction feature information includes feature data of multiple focus points of the construction project.

[0159] The second analysis module is used to analyze the construction feature information to obtain mapping feature information; the mapping feature information is used to represent the classification data corresponding to each focus point of the construction feature information.

[0160] The third analysis module is used to analyze the mapping feature information and determine the matching result; wherein the matching result is used to indicate the matching degree between the mapping feature information and the preset focus point.

[0161] The determination module is used to obtain the event response level based on the matching result.

[0162] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0163] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above modules is used as an example for illustration. In practical applications, the above functions can be assigned to different modules as needed, that is, the internal structure of the system can be divided into different modules to complete all or part of the functions described above. The modules in the embodiment can be integrated into a processing unit, or each module can exist physically alone, or two or more modules can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the modules are only for the convenience of distinguishing from each other, and are not used to limit the scope of protection of this application. The specific working process of the modules in the above system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0164] The embodiment of the present application also provides an intelligent supervision on-site data analysis device. Fig.12 This is a schematic diagram of the structure of the intelligent monitoring station data analysis device 6 provided in one embodiment of the present application. Fig.12 As shown, the intelligent monitoring station data analysis device 6 of this embodiment includes: at least one processor 60 ( Fig.12 Only one is shown), at least one memory 61 ( Fig.12Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the intelligent supervision on-site data analysis device 6 implements the steps in any of the above-mentioned intelligent supervision on-site data analysis method embodiments, or the intelligent supervision on-site data analysis device 6 implements the functions of each module in the above-mentioned system embodiments.

[0165] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the intelligent supervision station data analysis device 6.

[0166] The intelligent monitoring station data analysis device 6 can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The intelligent monitoring station data analysis device can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Fig.12 It is only an example of the intelligent monitoring side station data analysis device 6 and does not constitute a limitation of the intelligent monitoring side station data analysis device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, buses, etc.

[0167] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0168] In some embodiments, the memory 61 may be an internal storage unit of the intelligent supervision station data analysis device 6, such as a hard disk or memory of the intelligent supervision station data analysis device 6. In other embodiments, the memory 61 may also be an external storage device of the intelligent supervision station data analysis device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the intelligent supervision station data analysis device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the intelligent supervision station data analysis device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0169] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0170] An embodiment of the present application provides a computer program product. When the computer program product runs on an intelligent supervision on-site data analysis device, the intelligent supervision on-site data analysis device implements the steps in any of the above-mentioned method embodiments.

[0171] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the intelligent supervision station data analysis device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0172] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0173] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0174] In the embodiments provided in the present application, it should be understood that the disclosed intelligent supervision on-site data analysis equipment and system can be implemented in other ways. For example, the intelligent supervision on-site data analysis system embodiment described above is only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0175] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for analyzing data from an intelligent monitoring station, characterized in that: include: Acquire a construction site image; wherein the construction site image is used to reflect the real-time picture of the project; Analyze the construction site image to obtain construction feature information; wherein the construction feature information includes feature data of multiple focus points of the construction project, and the feature data of the multiple focus points include the location of construction equipment, the activities of construction personnel, the stacking location of construction materials, and the construction progress; Analyze the construction feature information to obtain mapping feature information; the mapping feature information is used to represent the classification data corresponding to each of the focus points of the construction feature information; Analyze the mapping feature information to determine a matching result; wherein the matching result is used to indicate the matching degree between the mapping feature information and the preset focus point; Obtaining an event response level based on the matching result; The step of analyzing the construction feature information to obtain mapping feature information includes: Analyze the construction feature information to obtain construction category information; wherein the construction category information is used to reflect the sub-projects of the construction project; Analyze the construction category information to obtain sub-project information; wherein the sub-project information is used to reflect multiple sub-projects in the sub-project; Mapping the sub-project information to a multi-dimensional space to obtain mapping feature information; wherein each dimension of the multi-dimensional space corresponds to one of the focus points in the sub-project information; The construction category information is obtained by analyzing the construction feature information, including: Analyze the construction feature information to obtain key feature points; Obtaining construction category information based on the key feature points; The key feature points are obtained by analyzing the construction feature information, including: Analyze the construction feature information to obtain feature values ​​of each of the focus points; Comparing the feature value with a preset threshold, screening out the feature value greater than the preset threshold, and obtaining a first feature group; Analyze the first feature group to obtain key feature points; The step of analyzing the first feature group to obtain key feature points includes: Associating any two of the focus points corresponding to the feature values ​​in the first feature group to obtain multiple feature groups; Acquire the association value of the feature group; wherein the association value is used to reflect the degree of association between the two focus points; Obtaining key feature points based on the association value; The obtaining of key feature points based on the association value includes: Matching the association value with a preset association value, and determining the feature group matching the preset association value as a key feature group; The feature values ​​of the focus points in the key feature group are compared, and the focus point with the larger feature value is determined as a key feature point.

2. The intelligent monitoring data analysis method according to claim 1, characterized in that: The analyzing according to the mapping feature information to determine the matching result includes: Acquire mapping data in the mapping feature information; wherein the mapping data is used to indicate data of each of the focus points mapped to a multi-dimensional space; Comparing the mapping data with standard mapping data to obtain a matching score; A matching result is determined according to the matching score.

3. The intelligent monitoring data analysis method according to claim 2, characterized in that: The comparing the mapping data with the standard mapping data to obtain a matching score comprises: Comparing the mapping data with the standard mapping data to obtain a difference value; wherein the difference value is used to reflect the distance between the mapping data and the standard mapping data in the multi-dimensional space; A matching score is obtained based on the difference value.

4. The intelligent monitoring data analysis method according to claim 3, characterized in that: Determining a matching result according to the matching score includes: Matching the matching score with a preset score range to obtain a matching level; A corresponding matching result is obtained based on the matching level.

5. The intelligent monitoring data analysis method according to claim 1, characterized in that: The obtaining of the event response level based on the matching result includes: When the matching result is lower than the first preset threshold, the event response level is high; wherein the measures corresponding to the high level include starting an emergency plan and sending a scheduling resource signal; And / or, obtaining the event response level based on the matching result further includes: When the matching result is lower than a second preset threshold, obtaining the number of construction workers; When the number of construction workers exceeds the preset number, the incident response level is intermediate; the corresponding measures for the intermediate level include sending a phone call or on-site broadcast alarm system instructions to correct or suspend the work; When the number of construction workers is lower than the preset number, the event response level is low; wherein the measures corresponding to the low level include sending an early warning signal.

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