Construction safety risk early warning method, device, electronic equipment and storage medium

Abstract

The present invention relates to the technical field of construction safety management, and in particular to a construction safety risk early warning method, device, electronic device and storage medium. The method of the present invention first obtains multiple monitoring results; then constructs multiple comparison matrices based on the multiple monitoring results and multiple monitoring sample sets, and determines multiple comparison scores based on the multiple comparison matrices; then determines multiple factor scores based on the multiple comparison scores and the weights of the multiple monitoring results; finally, binarizes the multiple factor scores respectively, and estimates the risk probability based on the binarization results to determine the safety risk level. The implementation method of the present invention determines the risk level by scoring the monitoring results and estimating the probability, so the risk level rating better reflects the correlation between the factors and the safety risk, and is more objective and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction safety management, and in particular to a building construction safety risk early warning method, device, electronic equipment and storage medium. Background Art

[0002] Construction project construction safety management is a systematic and comprehensive management, and its management content involves all aspects of construction production. Therefore, construction companies must adhere to the principle of "safety first, prevention first, scientific management and control, and comprehensive governance" in safety management, formulate safety policies, plans and measures, improve the safety production organization management system and inspection system, and strengthen construction safety management.

[0003] Construction project safety management mainly includes five aspects: formulating safety policies, improving the safety management organization system, safety production management planning and implementation, safety production management performance assessment, and safety management performance summary. Among them, risk analysis is an important part of risk management.

[0004] In the prior art, the analysis of risk mainly relies on indexing the factors that affect the safety risk and evaluating the risk level according to the degree of deterioration of the index. This analysis method has certain blindness and subjectivity, and cannot well reflect the degree of correlation between the factors and the safety risk.

[0005] Based on this, it is necessary to develop and design a construction safety risk early warning method. Summary of the invention

[0006] The embodiments of the present invention provide a construction safety risk early warning method, device, electronic device and storage medium, which are used to solve the problem that the analysis of construction safety risks in the prior art cannot well reflect the degree of correlation between factors and safety risks.

[0007] In a first aspect, an embodiment of the present invention provides a construction safety risk early warning method, comprising:

[0008] Obtaining a plurality of monitoring results, wherein the monitoring results are obtained based on factors affecting the safety of the construction project;

[0009] Constructing a plurality of comparison matrices according to the plurality of monitoring results and the plurality of monitoring sample sets, and determining a plurality of comparison scores according to the plurality of comparison matrices, wherein the plurality of comparison scores correspond to the plurality of monitoring results, and the comparison matrices represent the severity of the monitoring results relative to the samples in the monitoring sample set;

[0010] Determining multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results, wherein the weights of the multiple monitoring results represent the degree of influence of the factors on the safety of the construction project;

[0011] The multiple factor scores are binarized respectively, and risk probability estimation is performed based on the binarization results to determine the safety risk level.

[0012] In a possible implementation, constructing multiple comparison matrices according to the multiple monitoring results and the multiple monitoring sample sets, and determining multiple comparison scores according to the multiple comparison matrices includes:

[0013] For each of the multiple monitoring results, perform the following steps:

[0014] Selecting data with the same factors as the monitoring results from the multiple monitoring sample sets and combining them with the monitoring results to form a result queue;

[0015] Using the result pairs as row indicators and column indicators to fill in elements of a comparison matrix, wherein the elements of the comparison matrix represent the comparison results of the severity of the elements corresponding to the row indicators and the elements corresponding to the column indicators;

[0016] Calculate the maximum eigenvalue of the comparison matrix, and use the maximum eigenvalue of the comparison matrix as the comparison score corresponding to the monitoring result.

[0017] In a possible implementation, the weights of the multiple monitoring results are determined by factor comparison analysis, including:

[0018] Acquire multiple factors corresponding to the multiple monitoring results;

[0019] Arranging the multiple factors to obtain a factor column;

[0020] Multiple target factors are taken out from the factor column in turn, and the multiple target factors are compared with the factors in the factor column respectively to obtain multiple data queues corresponding to the multiple target factors, and the multiple data queues are constructed into a matrix with characteristic vectors, and multiple elements of the characteristic vector are used as weights of the multiple monitoring results.

[0021] In a possible implementation, taking out a plurality of target factors from the factor column in sequence, comparing the plurality of target factors with the factors in the factor column respectively to obtain a plurality of data queues corresponding to the plurality of target factors, constructing a matrix of feature vectors from the plurality of data queues, and using a plurality of elements of the feature vector as weights of the plurality of monitoring results, includes:

[0022] Obtaining a position indication and initializing the position indication;

[0023] Taking out a target factor from the factor list according to the position indication;

[0024] Comparing the influence of multiple factors in the factor column and the target element on the safety of the construction project in sequence, and quantifying the comparison results, thereby obtaining a data queue;

[0025] adding the data queue to a weight judgment matrix according to the position indication;

[0026] If the position indication does not reach the end of the factor column, the position indication is offset, and the process jumps to the step of extracting the target factor from the factor column according to the position indication;

[0027] Calculate the vector corresponding to the maximum eigenvalue of the weight judgment matrix, and use the vector corresponding to the maximum eigenvalue of the weight judgment matrix as the first vector;

[0028] Taking the unit vector of the first vector as a weight vector;

[0029] According to the correspondence between the multiple monitoring results and the factor column, multiple elements of the weight vector are used as weights of the multiple monitoring results.

[0030] In a possible implementation, determining multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results includes:

[0031] Determining weights of the plurality of monitoring results corresponding to the plurality of comparison scores according to the plurality of comparison scores;

[0032] According to the correspondence between the multiple comparison scores and the weights of the multiple monitoring results, the products of the multiple comparison scores and the weights of the multiple monitoring results are calculated respectively to obtain multiple factor scores.

[0033] In a possible implementation, binarizing the scores of the multiple factors respectively, and estimating the risk probability according to the binarization results to determine the security risk level, includes:

[0034] Acquire multiple factor division thresholds, multiple factor state probabilities, security risk probabilities, and multiple first conditional probabilities, wherein the multiple factor division thresholds correspond to the multiple scores, and the first conditional probability represents the probability of the factor state when the security risk occurs;

[0035] According to the multiple factor division thresholds, the multiple factor scores are binarized to obtain multiple factor states;

[0036] Determine a second conditional probability according to the multiple factor states, the multiple factor state probabilities, the security risk probability, and the multiple first conditional probabilities, wherein the second conditional probability represents the probability of the security risk occurring under the multiple factor states;

[0037] The security risk level is determined according to the second conditional probability and the security risk classification.

[0038] In a possible implementation manner, determining the second conditional probability according to the multiple factor states, the multiple factor state probabilities, the security risk probability, and the multiple first conditional probabilities includes:

[0039] The second conditional probability is determined according to the first formula, the multiple factor states, the multiple factor state probabilities, the security risk probability and the multiple first conditional probabilities, wherein the first formula is:

[0040]

[0041] Where, P(R|S1S2…S N ) is the second conditional probability, P(S n |R) is the first conditional probability corresponding to the nth factor state, P(R) is the safety risk probability, P(S n ) is the probability of the nth factor state.

[0042] In a second aspect, an embodiment of the present invention provides a construction safety risk early warning device, which is used to implement the construction safety risk early warning method described in the first aspect or any possible implementation of the first aspect, wherein the construction safety risk early warning device includes:

[0043] A monitoring result acquisition module, used to acquire multiple monitoring results, wherein the monitoring results are acquired based on factors affecting the safety of the construction project;

[0044] A monitoring result quantification module, used to construct a plurality of comparison matrices according to the plurality of monitoring results and the plurality of monitoring sample sets, and determine a plurality of comparison scores according to the plurality of comparison matrices, wherein the plurality of comparison scores correspond to the plurality of monitoring results, and the comparison matrices represent the severity of the monitoring results relative to the samples in the monitoring sample set;

[0045] A monitoring scoring module, used to determine multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results, wherein the weights of the multiple monitoring results represent the degree of influence of the factors on the safety of the construction project;

[0046] as well as,

[0047] The risk warning module is used to perform binarization processing on the scores of the multiple factors respectively, and perform risk probability estimation based on the results of the binarization processing to determine the safety risk level.

[0048] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0049] In a fourth aspect, an embodiment of the present invention 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 of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0050] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0051] The embodiment of the present invention discloses a construction safety risk early warning method, which first obtains multiple monitoring results, wherein the monitoring results are obtained based on factors affecting the safety of the construction project; then constructs multiple comparison matrices according to the multiple monitoring results and multiple monitoring sample sets, and determines multiple comparison scores according to the multiple comparison matrices, wherein the multiple comparison scores correspond to the multiple monitoring results, and the comparison matrix represents the severity of the monitoring results relative to the samples of the monitoring sample set; then determines multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results, wherein the weights of the multiple monitoring results represent the degree of influence of the factors on the safety of the construction project; finally, the multiple factor scores are binarized respectively, and the risk probability is estimated according to the result of the binarization to determine the safety risk level. The embodiment of the present invention quantifies the factor scores obtained by comparing the monitoring results with the existing samples, and after binarizing the factor scores, the probability of risk occurrence is evaluated according to the result of the binarization to determine the safety risk level. Since the risk level is determined by scoring the monitoring results and the probability estimation, the risk level rating better reflects the correlation between the factors and the safety risk, and is more objective and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 It is a flow chart of a construction safety risk early warning method provided by an embodiment of the present invention;

[0054] Figure 2 It is a functional block diagram of a construction safety risk early warning device provided by an embodiment of the present invention;

[0055] Figure 3 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0058] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0059] Figure 1 A flowchart of a construction safety risk early warning method provided in an embodiment of the present invention.

[0060] like Figure 1 As shown, it shows a flowchart of the implementation of the construction safety risk early warning method provided by the embodiment of the present invention, which is described in detail as follows:

[0061] In step 101, a plurality of monitoring results are obtained, wherein the monitoring results are obtained based on factors affecting the safety of a construction project.

[0062] Exemplarily, the monitoring results are obtained based on factors that affect the safety of construction projects. These factors include, from a broad perspective: people, machines, materials, methods, environment, and measurement. Specifically, they include human skill factors, the status of equipment, the status of raw and auxiliary materials, the degree of perfection of operating procedures and their implementation, environmental monitoring conditions, and the calibration of measuring equipment and safety equipment.

[0063] It can be seen that among the monitoring results obtained based on the above factors, some factors cannot directly obtain quantitative results, and the influence and correlation degree of the factors on the safety risks are not clear. The implementation mode of the present invention determines the score of the monitoring result by comparing with the existing data samples, and determines the weight of the factor by comparing the factors. The risk level associated with the monitoring result is determined by combining the weight, score and risk probability. The score, weight determination and risk assessment will be discussed below.

[0064] In step 102, multiple comparison matrices are constructed based on the multiple monitoring results and the multiple monitoring sample sets, and multiple comparison scores are determined based on the multiple comparison matrices, wherein the multiple comparison scores correspond to the multiple monitoring results, and the comparison matrices represent the severity of the monitoring results relative to the samples in the monitoring sample set.

[0065] In some implementations, step 102 includes:

[0066] For each of the multiple monitoring results, perform the following steps:

[0067] Selecting data with the same factors as the monitoring results from the multiple monitoring sample sets and combining them with the monitoring results to form a result queue;

[0068] Using the result pairs as row indicators and column indicators to fill in elements of a comparison matrix, wherein the elements of the comparison matrix represent the comparison results of the severity of the elements corresponding to the row indicators and the elements corresponding to the column indicators;

[0069] Calculate the maximum eigenvalue of the comparison matrix, and use the maximum eigenvalue of the comparison matrix as the comparison score corresponding to the monitoring result.

[0070] Exemplarily, the score of the monitoring sample in the embodiment of the present invention is obtained by comparing with the existing data samples. Specifically, each monitoring result is grouped together with the factor data of the same type in multiple monitoring sample sets to obtain a result queue. Then, the result queue is used as a row indicator and a column indicator to fill the comparison matrix. For example, in one scenario, the result queue is A1, A2, ..., An (A1 is the monitoring result), and the row indications are compared with the column indications one by one, and the comparison results are placed in the corresponding matrix positions. For example, A2 in the row indication is compared with A3 in the column indication. The comparison result shows that the severity of A2 is higher than A3, and the severity value is 5, then the element of the 2nd row and the 3rd column is set to 5, and multiple positions in the comparison matrix are filled in the same way.

[0071] After filling, the maximum eigenvalue of the comparison matrix is ​​calculated and used as the comparison score of the monitoring results.

[0072] In step 103, a plurality of factor scores are determined according to the plurality of comparison scores and the weights of the plurality of monitoring results, wherein the weights of the plurality of monitoring results represent the degree of influence of the factors on the safety of the construction project.

[0073] In some embodiments, the weights of the multiple monitoring results are determined by factor comparison analysis, including:

[0074] Acquire multiple factors corresponding to the multiple monitoring results;

[0075] Arranging the multiple factors to obtain a factor column;

[0076] Multiple target factors are taken out from the factor column in turn, and the multiple target factors are compared with the factors in the factor column respectively to obtain multiple data queues corresponding to the multiple target factors, and the multiple data queues are constructed into a matrix with characteristic vectors, and multiple elements of the characteristic vector are used as weights of the multiple monitoring results.

[0077] In some implementations, sequentially taking out a plurality of target factors from the factor column, comparing the plurality of target factors with the factors in the factor column respectively to obtain a plurality of data queues corresponding to the plurality of target factors, constructing a matrix of feature vectors from the plurality of data queues, and using a plurality of elements of the feature vector as weights of the plurality of monitoring results, comprises:

[0078] Obtaining a position indication and initializing the position indication;

[0079] Taking out a target factor from the factor list according to the position indication;

[0080] Comparing the influence of multiple factors in the factor column and the target element on the safety of the construction project in sequence, and quantifying the comparison results, thereby obtaining a data queue;

[0081] adding the data queue to a weight judgment matrix according to the position indication;

[0082] If the position indication does not reach the end of the factor column, the position indication is offset, and the process jumps to the step of extracting the target factor from the factor column according to the position indication;

[0083] Calculate the vector corresponding to the maximum eigenvalue of the weight judgment matrix, and use the vector corresponding to the maximum eigenvalue of the weight judgment matrix as the first vector;

[0084] Taking the unit vector of the first vector as a weight vector;

[0085] According to the correspondence between the multiple monitoring results and the factor column, multiple elements of the weight vector are used as weights of the multiple monitoring results.

[0086] In some embodiments, determining multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results includes:

[0087] Determining weights of the plurality of monitoring results corresponding to the plurality of comparison scores according to the plurality of comparison scores;

[0088] According to the correspondence between the multiple comparison scores and the weights of the multiple monitoring results, the products of the multiple comparison scores and the weights of the multiple monitoring results are calculated respectively to obtain multiple factor scores.

[0089] Exemplarily, with respect to factor scores, an embodiment of the present invention first determines the weight of the monitoring results by comparison, in other words, the weight of the factors corresponding to the monitoring results. For example, the monitoring results are obtained based on six factors: man, machine, material, method, environment, and measurement. At this time, it is necessary to determine the weights of these six factors.

[0090] In determining the weights, first arrange the factors to obtain a factor column, then take out the target factor from the factor column according to the position indication, and compare the impact of the target factor on the security risk with multiple factors in the factor column. Generally, the greater the impact, the larger the value. The comparison results are combined into a data queue, and this data queue is added to the weight judgment matrix according to the position indication. When not all factors are taken out from the factor column, the position indication is offset and the above steps are repeated to obtain the weight judgment matrix.

[0091] The maximum eigenvalue of the weight judgment matrix is ​​calculated, and the eigenvector corresponding to the maximum eigenvalue is determined based on the maximum eigenvalue, and the unit vector of the eigenvector is used as the weight vector. The multiple elements of the weight vector are the weights of the monitoring results.

[0092] After obtaining the weights, the product of the weights and the comparison scores is calculated, and the product is used as the factor score.

[0093] In step 104, the multiple factor scores are binarized respectively, and risk probability estimation is performed based on the binarization results to determine the security risk level.

[0094] In some implementations, step 104 includes:

[0095] Acquire multiple factor division thresholds, multiple factor state probabilities, security risk probabilities, and multiple first conditional probabilities, wherein the multiple factor division thresholds correspond to the multiple scores, and the first conditional probability represents the probability of the factor state when the security risk occurs;

[0096] According to the multiple factor division thresholds, the multiple factor scores are binarized to obtain multiple factor states;

[0097] Determine a second conditional probability according to the multiple factor states, the multiple factor state probabilities, the security risk probability, and the multiple first conditional probabilities, wherein the second conditional probability represents the probability of the security risk occurring under the multiple factor states;

[0098] The security risk and the like are determined according to the second conditional probability and the security risk classification.

[0099] In some implementations, determining the second conditional probability according to the multiple factor states, the multiple factor state probabilities, the security risk probability, and the multiple first conditional probabilities includes:

[0100] The second conditional probability is determined according to the first formula, the multiple factor states, the multiple factor state probabilities, the security risk probability and the multiple first conditional probabilities, wherein the first formula is:

[0101]

[0102] Where, P(R|S1S2…S N ) is the probability of safety risk occurring under multiple factors, P(S n |R) is the probability of the bth factor state when the security risk occurs, P(R) is the security risk probability, P(S n ) is the probability of the nth factor state.

[0103] Exemplarily, the scores of the above-mentioned multiple monitoring results are actually the scores of the corresponding factors, which affect construction safety. In order to associate these factors with safety risks, the embodiment of the present invention first binarizes the factor scores according to the threshold. For example, in one scenario, the factor value obtained by the above steps is less than 1, and the threshold is set to 0.5. The factor score greater than the threshold is set to 1, which means that it has a more serious impact on the safety risk. The factor score less than or equal to the threshold is set to 0, which means that the impact on the safety risk is small and can be ignored. The binarized factor score actually indicates the state of the factor. The factor state is combined with the probability of safety risk, the probability of factor state, and the conditional probability of factor state under safety risk conditions to determine the probability of safety risk under the current multiple factor states.

[0104] Specifically, the embodiment of the present invention applies the first formula to determine the probability of risk occurrence under the current factor state:

[0105]

[0106] Where, P(R|S1S2…S N ) is the probability of safety risk occurring under multiple factors, P(S n |R) is the probability of the nth factor state when the security risk occurs, P(R) is the security risk probability, P(S n ) is the probability of the nth factor state.

[0107] By comparing the probability calculated according to the first formula with the risk classification, the risk level can be determined. When the risk level is high, immediate measures should be taken to inspect and correct the risk source.

[0108] The implementation method of the construction safety risk early warning method of the present invention first obtains multiple monitoring results, wherein the monitoring results are obtained based on factors affecting the safety of the construction project; then constructs multiple comparison matrices according to the multiple monitoring results and multiple monitoring sample sets, and determines multiple comparison scores according to the multiple comparison matrices, wherein the multiple comparison scores correspond to the multiple monitoring results, and the comparison matrix represents the severity of the monitoring results relative to the samples of the monitoring sample set; then determines multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results, wherein the weights of the multiple monitoring results represent the degree of influence of the factors on the safety of the construction project; finally, the multiple factor scores are binarized respectively, and the risk probability is estimated according to the results of the binarization processing to determine the safety risk level. The implementation method of the present invention quantifies the factor scores obtained by comparing the monitoring results with the existing samples, and after binarizing the factor scores, the probability of risk occurrence is evaluated according to the results of the binarization processing to determine the safety risk level. Since the risk level is determined by scoring the monitoring results and estimating the probability, the risk level rating better reflects the correlation between the factors and the safety risk, and is more objective and accurate.

[0109] It should be understood that the size of the serial numbers of the steps in the above implementation 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 implementation method of the present invention.

[0110] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0111] Figure 2 This is a functional block diagram of a construction safety risk early warning device provided by an embodiment of the present invention, referring to Figure 2 The construction safety risk early warning device includes: a monitoring result acquisition module 201, a monitoring result quantification module 202, a monitoring scoring module 203 and a risk early warning module 204, wherein:

[0112] A monitoring result acquisition module 201 is used to acquire a plurality of monitoring results, wherein the monitoring results are acquired based on factors affecting the safety of the construction project;

[0113] A monitoring result quantification module 202, configured to construct a plurality of comparison matrices according to the plurality of monitoring results and the plurality of monitoring sample sets, and determine a plurality of comparison scores according to the plurality of comparison matrices, wherein the plurality of comparison scores correspond to the plurality of monitoring results, and the comparison matrices represent the severity of the monitoring results relative to the samples in the monitoring sample set;

[0114] A monitoring scoring module 203, configured to determine a plurality of factor scores according to the plurality of comparison scores and the weights of the plurality of monitoring results, wherein the weights of the plurality of monitoring results represent the degree of influence of the factors on the safety of the construction project;

[0115] The risk warning module 204 is used to perform binarization processing on the scores of the multiple factors respectively, and perform risk probability estimation based on the binarization processing results to determine the safety risk level.

[0116] Figure 3 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 300 and a memory 301, wherein the memory 301 stores a computer program 302 that can be run on the processor 300. When the processor 300 executes the computer program 302, the steps in the above-mentioned construction safety risk early warning method and the embodiment are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0117] Exemplarily, the computer program 302 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 301 and executed by the processor 300 to implement the present invention.

[0118] The electronic device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 3 may include, but is not limited to, a processor 300 and a memory 301. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 3 and does not constitute a limitation of the electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 3 may also include input and output devices, network access devices, buses, etc.

[0119] The processor 300 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.

[0120] The memory 301 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 301 may also be an external storage device of the electronic device 3, 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 electronic device 3. Further, the memory 301 may also include both an internal storage unit of the electronic device 3 and an external storage device. The memory 301 is used to store the computer program 302 and other programs and data required by the electronic device 3. The memory 301 may also be used to temporarily store data that has been output or is to be output.

[0121] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned 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 functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0122] In the above-mentioned 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.

[0123] Those skilled 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 the present invention.

[0124] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated module / 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 invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various methods and device implementation methods. 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 may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0128] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention 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 various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A construction safety risk early warning method, characterized in that: include: Obtaining a plurality of monitoring results, wherein the monitoring results are obtained based on factors affecting the safety of the construction project; Constructing a plurality of comparison matrices according to the plurality of monitoring results and the plurality of monitoring sample sets, and determining a plurality of comparison scores according to the plurality of comparison matrices, wherein the plurality of comparison scores correspond to the plurality of monitoring results, and the comparison matrices represent the severity of the monitoring results relative to the samples in the monitoring sample set; Determining multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results, wherein the weights of the multiple monitoring results represent the degree of influence of the factors on the safety of the construction project; Binarizing the scores of the multiple factors respectively, and estimating the risk probability according to the binarization results to determine the safety risk level; in, The binarization of the scores of the multiple factors is respectively performed, and risk probability estimation is performed according to the binarization results to determine the security risk level, including: Acquire multiple factor division thresholds, multiple factor state probabilities, security risk probabilities, and multiple first conditional probabilities, wherein the multiple factor division thresholds correspond to the multiple scores, and the first conditional probability represents the probability of the factor state when the security risk occurs; According to the multiple factor division thresholds, the multiple factor scores are binarized to obtain multiple factor states; Determine a second conditional probability according to the multiple factor states, the multiple factor state probabilities, the security risk probability, and the multiple first conditional probabilities, wherein the second conditional probability represents the probability of the security risk occurring under the multiple factor states; Determining the security risk level according to the second conditional probability and the security risk classification; in, The determining the second conditional probability according to the multiple factor states, the multiple factor state probabilities, the security risk probability and the multiple first conditional probabilities includes: The second conditional probability is determined according to the first formula, the multiple factor states, the multiple factor state probabilities, the security risk probability and the multiple first conditional probabilities, wherein the first formula is: Where, P(R|S1S2…S N ) is the second conditional probability, P(S n |R) is the first conditional probability corresponding to the nth factor state, P(R) is the safety risk probability, P(S n ) is the probability of the nth factor state.

2. The construction safety risk early warning method according to claim 1 is characterized in that: The constructing a plurality of comparison matrices according to the plurality of monitoring results and the plurality of monitoring sample sets, and determining a plurality of comparison scores according to the plurality of comparison matrices, comprises: For each of the multiple monitoring results, perform the following steps: Selecting data with the same factors as the monitoring results from the multiple monitoring sample sets and combining them with the monitoring results to form a result queue; Using the result pairs as row indicators and column indicators to fill in elements of a comparison matrix, wherein the elements of the comparison matrix represent the comparison results of the severity of the elements corresponding to the row indicators and the elements corresponding to the column indicators; Calculate the maximum eigenvalue of the comparison matrix, and use the maximum eigenvalue of the comparison matrix as the comparison score corresponding to the monitoring result.

3. The construction safety risk early warning method according to claim 1 is characterized in that: The weights of the multiple monitoring results are determined by comparative analysis of factors, including: Acquire multiple factors corresponding to the multiple monitoring results; Arranging the multiple factors to obtain a factor column; Multiple target factors are taken out from the factor column in turn, and the multiple target factors are compared with the factors in the factor column respectively to obtain multiple data queues corresponding to the multiple target factors, and the multiple data queues are constructed into a matrix with characteristic vectors, and multiple elements of the characteristic vector are used as weights of the multiple monitoring results.

4. The construction safety risk early warning method according to claim 3 is characterized in that: The method of sequentially taking out a plurality of target factors from the factor column, comparing the plurality of target factors with the factors in the factor column respectively to obtain a plurality of data queues corresponding to the plurality of target factors, constructing a matrix of feature vectors from the plurality of data queues, and using a plurality of elements of the feature vector as weights of the plurality of monitoring results includes: Obtaining a position indication and initializing the position indication; Taking out a target factor from the factor list according to the position indication; Comparing the influence of multiple factors in the factor column and the target element on the safety of the construction project in sequence, and quantifying the comparison results, thereby obtaining a data queue; adding the data queue to a weight judgment matrix according to the position indication; If the position indication does not reach the end of the factor column, the position indication is offset, and the process jumps to the step of extracting the target factor from the factor column according to the position indication; Calculate the vector corresponding to the maximum eigenvalue of the weight judgment matrix, and use the vector corresponding to the maximum eigenvalue of the weight judgment matrix as the first vector; Taking the unit vector of the first vector as a weight vector; According to the correspondence between the multiple monitoring results and the factor column, multiple elements of the weight vector are used as weights of the multiple monitoring results.

5. The construction safety risk early warning method according to claim 1 is characterized in that: Determining multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results includes: Determining weights of the plurality of monitoring results corresponding to the plurality of comparison scores according to the plurality of comparison scores; According to the correspondence between the multiple comparison scores and the weights of the multiple monitoring results, the products of the multiple comparison scores and the weights of the multiple monitoring results are calculated respectively to obtain multiple factor scores.

6. A construction safety risk early warning device, characterized in that: Used to implement the construction safety risk early warning method according to any one of claims 1 to 5, the construction safety risk early warning device comprises: A monitoring result acquisition module, used to acquire multiple monitoring results, wherein the monitoring results are acquired based on factors affecting the safety of the construction project; A monitoring result quantification module, used to construct a plurality of comparison matrices according to the plurality of monitoring results and the plurality of monitoring sample sets, and determine a plurality of comparison scores according to the plurality of comparison matrices, wherein the plurality of comparison scores correspond to the plurality of monitoring results, and the comparison matrices represent the severity of the monitoring results relative to the samples in the monitoring sample set; A monitoring scoring module, used to determine multiple factor scores according to the multiple comparison scores and the weights of the multiple monitoring results, wherein the weights of the multiple monitoring results represent the degree of influence of the factors on the safety of the construction project; as well as, The risk warning module is used to perform binarization processing on the scores of the multiple factors respectively, and perform risk probability estimation based on the results of the binarization processing to determine the safety risk level.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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