A construction enterprise management method based on artificial intelligence

Abstract

The present invention provides a construction enterprise management method based on artificial intelligence, which belongs to the field of enterprise management technology, and specifically includes: determining the monitoring reliability coefficients of different safety risk points and the risk monitoring points of construction sites under construction based on the setting data of the Internet of Things monitoring device in the safety risk point and the analysis results of the historical monitoring data of different Internet of Things monitoring devices; determining the safety monitoring risk coefficients of different construction sites under construction and the risk construction sites based on the construction data of the risk monitoring points of different construction sites under construction and the monitoring reliability coefficients; obtaining the safety risk points of the risk construction sites and the construction data of different safety risk points; and combining the safety monitoring risk coefficients of the risk construction sites, using a pre-trained AI model to determine the on-site safety inspection strategy of the risk construction sites, thereby ensuring the safety of the construction enterprise.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and in particular relates to a construction enterprise management method based on artificial intelligence. Background Art

[0002] When construction companies are carrying out construction, there are many safety hazards on the construction site. Therefore, in order to achieve safety management of construction companies, in CN202410143900.5 "Smart Construction Site Safety Supervision Method and System Based on Internet of Things", through effective and comprehensive monitoring of various areas on the construction site, the corresponding areas can be reasonably and accurately graded based on regional monitoring feedback information, significantly improving the construction efficiency and construction safety of the construction site. However, there are the following technical problems:

[0003] When conducting safety management for construction companies, since there are a large number of construction sites under construction at the same time, how to generate differentiated safety risk supervision strategies based on the differences in construction data of different construction sites has become a technical problem that needs to be solved urgently.

[0004] In response to the above technical problems, the present invention provides a construction enterprise management method based on artificial intelligence. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a construction enterprise management method based on artificial intelligence is provided.

[0007] A construction enterprise management method based on artificial intelligence, specifically comprising:

[0008] S1 obtains the distribution of the construction sites under construction of the construction enterprise, and when it is determined that the difficulty of the on-site safety inspection of the construction enterprise does not meet the requirements in combination with the number of the construction sites under construction and the safety risk point data of the construction enterprise, proceeds to the next step;

[0009] S2 determines the setting data of the Internet of Things monitoring devices in the safety risk points of different construction sites under construction, and determines the monitoring reliability coefficients of different safety risk points in combination with the analysis results of the historical monitoring data of the different Internet of Things monitoring devices, and determines the risk monitoring points of the construction sites under construction using the monitoring reliability coefficients;

[0010] S3 determines the safety monitoring risk coefficients of different construction sites under construction and the risky construction sites based on the construction data of risk monitoring points and monitoring reliability coefficients of different construction sites under construction;

[0011] S4 obtains the safety risk points of the risky construction site and the construction data of different safety risk points, and determines the on-site safety inspection strategy of the risky construction site by using a pre-trained AI model in combination with the safety monitoring risk coefficient of the risky construction site.

[0012] The beneficial effects of the present invention are:

[0013] 1. Risky construction sites are identified based on the construction data and monitoring reliability coefficients of risk monitoring points at different construction sites under construction. This not only takes into account the impact of safety risks on the construction progress and construction personnel at risk monitoring points due to differences in the number of construction personnel and construction volume, but also takes into account the differences in the reliability of monitoring devices at different risk monitoring points. This enables the screening of construction sites with higher risks from multiple angles, and lays the foundation for generating differentiated strategies for on-site safety inspections.

[0014] 2. Based on the safety risk points of risky construction sites, the construction data of different safety risk points and the safety monitoring risk coefficient of risky construction sites, the on-site safety inspection strategy of risky construction sites is determined. This not only ensures the differentiated needs of on-site safety inspections at risky construction sites with deviations in risk levels due to differences in the number of construction personnel, but also ensures the differentiated needs of on-site safety inspections at risky construction sites due to differences in the unreliability of monitoring devices. On the basis of ensuring the processing efficiency of on-site safety inspections, it also avoids the emergence of technical problems that cannot be reliably monitored for on-site construction safety.

[0015] A further technical solution is that the distribution of the construction sites under construction includes locations of different construction sites under construction.

[0016] A further technical solution is that the safety risk point data includes the number of different types of safety risk points, which is specifically determined according to the type of the construction site under construction.

[0017] A further technical solution is that the method for determining the on-site safety inspection strategy of the risky construction site is:

[0018] Determine the average daily number of construction workers at different safety risk points based on the construction data at different safety risk points;

[0019] Taking the average daily number of construction workers at different safety risk points, the number of safety risk points at risky construction sites, and the safety monitoring risk coefficient of risky construction sites as input, a pre-trained AI model is used to determine the matching coefficient between the risky construction sites and different on-site safety inspection strategies;

[0020] An on-site safety inspection strategy for the risky construction site is determined based on the matching coefficient.

[0021] A further technical solution is to use the on-site safety inspection strategy with the largest matching coefficient as the on-site safety inspection strategy for the risky construction site.

[0022] A further technical solution is that the on-site safety inspection strategy for the risky construction site includes an inspection cycle for the on-site safety inspection.

[0023] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0026] Figure 1 It is a flowchart of a construction enterprise management method based on artificial intelligence;

[0027] Figure 2 It is a flow chart of a method for determining the monitoring reliability coefficient of a safety risk point;

[0028] Figure 3 is a flow chart of a method for determining a monitoring risk coefficient of a risk point monitoring device;

[0029] Figure 4 It is a flow chart of a method for determining a safety monitoring risk factor for a construction site under construction. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0031] When conducting safety management of construction companies, since construction companies have a large number of construction sites at the same time, it is necessary to combine the differences in safety risk data of different construction sites to generate differentiated on-site safety risk supervision strategies to ensure the safety of construction companies' construction sites.

[0032] Specifically in this application, whether the monitoring equipment in the construction site can fully and accurately monitor the construction risks is used to determine the risk monitoring points in the construction site, and the number of risk monitoring points is used to implement different on-site safety monitoring strategies for the construction sites, specifically including the inspection cycle of on-site safety inspections.

[0033] Glossary:

[0034] 1. Difficulty of on-site safety inspection of construction enterprises: It is related to the distance between construction sites under construction, the number of construction sites under construction and the number of safety risk points. Specifically, in the present application, the difficulty of on-site safety inspection is determined by the total number of safety risk points in the construction sites under construction and the average distance between the construction sites under construction. In a possible embodiment, for example, the total number is 200 and the average distance is 100 kilometers. The corresponding preset inspection difficulty under the total number is 0.3, and the corresponding preset inspection difficulty under the average distance is 0.4, then the difficulty of on-site safety inspection is 0.7.

[0035] 2. Monitoring reliability coefficient: It is to illustrate whether the Internet of Things monitoring device can accurately monitor the construction site under construction. The safety risk points that cannot be accurately monitored are risk monitoring points. Specifically, in this application, the monitoring reliability coefficient can be determined by the product of the maximum value of the proportion of fault periods of the Internet of Things monitoring device on different dates and the proportion of the number of monitored safety hazards in the safety risk points. In a possible embodiment, for example, the maximum value of the proportion of fault periods is 0.2, and the proportion of monitored quantities is 0.9. The monitoring reliability coefficient is determined as the product of (1-0.2) and 0.9, which is 0.72, that is, the monitoring reliability coefficient is 0.72.

[0036] Safety monitoring risk coefficient: It is to determine the risky construction sites. For risky construction sites, a separate on-site safety inspection strategy needs to be adopted. For construction sites with different risks, only the preset on-site safety inspection strategy needs to be adopted. Specifically, in this application, the construction data of different risk monitoring points are used to determine the number of construction personnel at the risk monitoring points. For example, if the number of construction personnel is 10, the corresponding risk weight value is 0.1. Finally, the safety monitoring risk coefficient is determined based on the product of the risk weight value and the monitoring reliability coefficient. In a possible embodiment, for example, 0.1 multiplied by 0.7+0.3 multiplied by 0.9 equals 0.34, and the safety monitoring risk coefficient 1 minus 0.34 is 0.66.

[0037] On-site safety inspection strategy for risky construction sites: The safety risk points of risky construction sites, the number of construction workers at different safety risk points, and the safety monitoring risk coefficient of risky construction sites are used as inputs to the AI ​​model to determine the matching coefficient, and the on-site safety inspection strategy is determined using the range of the matching coefficient.

[0038] Specifically in this application, the steps for building the AI ​​model are:

[0039] 1. Data Preprocessing

[0040] Feature selection: Select features that contribute to the prediction target from the original data. In this application, the safety risk points of the risky construction site, the number of construction workers at different safety risk points, and the safety monitoring risk coefficient of the risky construction site are the input, and the matching coefficient is the output.

[0041] Dataset division: The cleaned and selected dataset is divided into training set, validation set and test set. Usually, the training set accounts for 70%-80% of the total data, the validation set accounts for 10%-15%, and the test set accounts for 10%-15%. The training set is used to train the model, the validation set is used to adjust the model parameters, and the test set is used to evaluate the prediction performance of the model.

[0042] 2. Network structure design

[0043] Determine the number of layers: The BP neural network contains at least three layers: an input layer, one or more hidden layers, and an output layer, where the number of hidden layers is 6.

[0044] 3. Initialize weights and biases

[0045] Before training, the weights and biases of the neural network need to be initialized. Use random numbers (such as normal distribution or uniform distribution) to initialize the weights and biases of the network.

[0046] 4. Forward Propagation

[0047] The input data is calculated by each layer and outputted through the activation function. The output is the matching coefficient, and the weights and biases in the network are adjusted according to the gradient of the loss function to reduce the prediction error. By calculating the gradient, the optimization algorithm is used to update the weights and biases in the network.

[0048] After the training is completed, the neural network model is used for prediction. Example

[0049] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a construction enterprise management method based on artificial intelligence is provided, which specifically includes:

[0050] S1 obtains the distribution of the construction sites under construction of the construction enterprise, and when it is determined that the difficulty of the on-site safety inspection of the construction enterprise does not meet the requirements in combination with the number of the construction sites under construction and the safety risk point data of the construction enterprise, proceeds to the next step;

[0051] Furthermore, the distribution of the construction sites under construction includes locations of different construction sites under construction.

[0052] It can be understood that the safety risk point data includes the number of different types of safety risk points, which is specifically determined according to the type of the construction site under construction.

[0053] Specifically, it is determined that the difficulty level of the on-site safety inspection of the construction enterprise does not meet the requirements, including:

[0054] Based on the distribution of construction sites under construction, the locations of different construction sites under construction are determined, and the interval distances between different construction sites under construction are determined based on the locations of different construction sites under construction, and the distance difficulty coefficient of the on-site safety inspection of the construction enterprise is determined using the interval distances;

[0055] Determine the number of different types of safety risk points at different construction sites through the safety risk point data at different construction sites, and determine the risk inspection difficulty coefficients at different construction sites using the number of different types of safety risk points;

[0056] The comprehensive risk inspection difficulty coefficient of the construction enterprise is determined based on the risk inspection difficulty coefficients of different construction sites under construction and the number of construction sites under construction, and the on-site inspection difficulty coefficient of the construction enterprise is determined in combination with the distance difficulty coefficient of the on-site safety inspection of the construction enterprise. Based on the on-site inspection difficulty coefficient, it is determined whether the on-site safety inspection difficulty of the construction enterprise meets the requirements.

[0057] Further, when the on-site inspection difficulty coefficient of the construction enterprise is not within a preset range, it is determined that the on-site safety inspection difficulty of the construction enterprise does not meet the requirements.

[0058] It should also be noted that the distance difficulty coefficient of the on-site safety inspection of the construction enterprise is determined by using a preset mapping function based on the sum of the spacing distances between different construction sites under construction of the construction enterprise.

[0059] It is understandable that when the difficulty of the on-site safety inspection of the construction enterprise meets the requirements, on-site safety inspections of different construction sites under construction will be carried out according to preset cycles.

[0060] It is also understandable that the difficulty of determining the on-site safety inspection of the construction enterprise does not meet the requirements, specifically including:

[0061] Based on the distribution of the construction sites under construction, the locations of different construction sites under construction are determined, and the interval distances between different construction sites under construction are determined based on the locations of the different construction sites under construction, and the distribution dispersion coefficient of the construction sites under construction of the construction enterprise is determined using the interval distances;

[0062] Determine the number of different types of safety risk points at different construction sites under construction using the safety risk point data of different construction sites under construction, and determine the total number of safety risk points using the number of different types of safety risk points and the number of construction sites under construction;

[0063] The on-site inspection difficulty coefficient of the construction enterprise is determined by utilizing the ratio of the total number of the safety risk points to the preset number and the product of the distribution dispersion coefficient. Based on the on-site inspection difficulty coefficient, it is determined whether the on-site safety inspection difficulty of the construction enterprise meets the requirements.

[0064] It is also necessary to further explain that the difficulty of on-site safety inspection of the construction enterprise does not meet the requirements, including:

[0065] S11 obtains the number of construction sites under construction of the construction enterprise, and determines whether the number of construction sites under construction of the construction enterprise is greater than the preset number of construction sites. If so, proceeds to the next step; if not, proceeds to step S14;

[0066] S12 determines the locations of different construction sites under construction based on the distribution of the construction sites under construction, and determines the distances between different construction sites under construction based on the locations of the different construction sites under construction, and judges whether the average value of the distances between different construction sites under construction meets the requirements. If so, proceed to the next step. If not, it is determined that the difficulty of the on-site safety inspection of the construction enterprise does not meet the requirements.

[0067] S13 determines the number of different types of safety risk points of different construction sites under construction through the safety risk point data of different construction sites under construction, and determines the total number of safety risk points using the number of different types of safety risk points and the number of the construction sites under construction, and judges whether the number of safety risk points meets the requirements. If so, proceeds to the next step; if not, determines that the difficulty of the on-site safety inspection of the construction enterprise does not meet the requirements;

[0068] S14 determines the number of different types of safety risk points of different construction sites under construction through the safety risk point data of different construction sites under construction, and determines the risk inspection difficulty coefficients of different construction sites under construction by using the number of different types of safety risk points, and determines whether the number of construction sites under construction whose risk inspection difficulty coefficients do not meet the requirements meets the requirements, if so, proceeds to the next step, if not, determines that the on-site safety inspection difficulty of the construction enterprise does not meet the requirements;

[0069] S15 uses the interval distance to determine the distribution dispersion coefficient of the construction sites under construction of the construction enterprise, and uses the ratio of the total number of the safety risk points to the preset number and the product of the distribution dispersion coefficient to determine the on-site inspection difficulty coefficient of the construction enterprise, and determines whether the on-site safety inspection difficulty of the construction enterprise meets the requirements based on the on-site inspection difficulty coefficient.

[0070] S2 determines the setting data of the Internet of Things monitoring devices in the safety risk points of different construction sites under construction, and determines the monitoring reliability coefficients of different safety risk points in combination with the analysis results of the historical monitoring data of the different Internet of Things monitoring devices, and determines the risk monitoring points of the construction sites under construction using the monitoring reliability coefficients;

[0071] Furthermore, the setting data of the Internet of Things monitoring device includes the setting quantity of different types of Internet of Things monitoring devices.

[0072] Specifically, the analysis result of the historical monitoring data includes the number of abnormal monitoring periods and the number of data anomalies in different abnormal monitoring periods.

[0073] It can be understood that the abnormal monitoring period is a period during which data anomalies exist.

[0074] Specifically, Figure 2 As shown, the method for determining the monitoring reliability coefficient of the safety risk point is:

[0075] Determine the setting data of different types of Internet of Things monitoring devices at different safety risk points based on the setting data of the Internet of Things monitoring devices at the safety risk points of the construction site under construction, and use them as risk point monitoring devices;

[0076] Determine the number of abnormal monitoring periods of different risk point monitoring devices through the analysis results of the historical monitoring data of the risk point monitoring device, and determine the monitoring risk coefficients of different risk point monitoring devices in combination with the number of abnormal data in different abnormal monitoring periods;

[0077] Based on the number of different types of risk point monitoring devices and the monitoring risk coefficients of the risk point monitoring devices, the reliability coefficient evaluation amounts of different types of risk point monitoring devices are determined, and the monitoring reliability coefficients of the safety risk points are determined using the preset weight coefficients and reliability coefficient evaluation amounts of the different types of risk point monitoring devices.

[0078] Furthermore, the monitoring reliability coefficient of the safety risk point has a value range between 0 and 1, wherein when the monitoring reliability coefficient of the safety risk point is not within a preset reliability coefficient interval, the safety risk point is determined to be a risk monitoring point.

[0079] It should be noted that if Figure 3 As shown, the method for determining the monitoring risk coefficient of the risk point monitoring device is:

[0080] Based on the number of data anomalies in different abnormal monitoring periods, determine the basic risk coefficients for different abnormal monitoring periods;

[0081] The monitoring risk coefficient of the risk point monitoring device is determined by multiplying the average value of the basic risk coefficients of different abnormal monitoring periods by the proportion of the number of abnormal monitoring periods.

[0082] It should also be noted that the method for determining the monitoring reliability coefficient of the safety risk point is:

[0083] Determine the setting data of different types of Internet of Things monitoring devices at different safety risk points based on the setting data of the Internet of Things monitoring devices at the safety risk points of the construction site under construction, and use them as risk point monitoring devices;

[0084] The number of different types of risk point monitoring devices is used to determine the basic reliability coefficients of different types of risk point monitoring devices, and the risk point monitoring devices with abnormal monitoring periods are determined based on the analysis results of historical monitoring data of different risk point monitoring devices, and are used as abnormal monitoring devices;

[0085] The reliability coefficient assessment amount of different types of risk point monitoring devices is determined by multiplying the proportion of the number of different types of abnormal monitoring devices in the risk point monitoring devices and the basic reliability coefficient, and the monitoring reliability coefficient of the safety risk point is determined by the preset weight coefficient of the different types of risk point monitoring devices and the reliability coefficient assessment amount.

[0086] It should also be noted that the method for determining the monitoring reliability coefficient of the safety risk point is:

[0087] S21 determines the setting data of different types of IoT monitoring devices at different security risk points based on the setting data of the IoT monitoring devices at the security risk points of the construction site under construction, and uses them as risk point monitoring devices to determine whether the number of risk point monitoring devices at the security risk points is greater than the preset number of monitoring devices. If so, proceed to the next step; if not, proceed to step S26;

[0088] S22 determines the risk point monitoring device with abnormal monitoring period based on the analysis results of the historical monitoring data of different risk point monitoring devices, and uses it as the abnormal monitoring device, and judges whether the number ratio of the abnormal monitoring devices meets the requirements. If so, it is determined that the security risk point does not belong to the risk monitoring point. If not, proceed to the next step;

[0089] S23 determines whether the quantity ratios of different types of abnormal monitoring devices meet the requirements. If yes, proceed to the next step. If not, proceed to step S26.

[0090] S24 determines the number of abnormal monitoring periods of different abnormal monitoring devices through the analysis result of the historical monitoring data of the abnormal monitoring device, and determines the monitoring risk coefficients of different abnormal monitoring devices in combination with the number of abnormal data in different abnormal monitoring periods;

[0091] S25 determines the reliability coefficient evaluation amount of different types of risk point monitoring devices based on the number of different types of risk point monitoring devices and the monitoring risk coefficients of the risk point monitoring devices, and determines whether there is a type of risk point monitoring device whose reliability coefficient evaluation amount does not meet the requirements. If so, proceed to the next step; if not, determine that the safety risk point does not belong to a risk monitoring point;

[0092] S26 determines the monitoring reliability coefficient of the safety risk point by using the preset weight coefficients and reliability coefficient evaluation amounts of different types of risk point monitoring devices.

[0093] S3 determines the safety monitoring risk coefficients of different construction sites under construction and the risky construction sites based on the construction data of risk monitoring points and monitoring reliability coefficients of different construction sites under construction;

[0094] Furthermore, the construction data of the risk monitoring point includes the average daily construction workload and the average daily number of construction personnel at the risk monitoring point.

[0095] Specifically, Figure 4 As shown, the method for determining the safety monitoring risk coefficient of the construction site under construction is:

[0096] Determine the average daily construction volume of different risk monitoring points based on the construction data of the risk monitoring points of the construction site under construction, and determine the safety risk progress impact coefficient of different risk monitoring points based on the average daily construction volume of different risk monitoring points;

[0097] Determine the safety risk personnel impact coefficient of different risk monitoring points by the average daily number of construction personnel at different risk monitoring points, and determine the safety risk monitoring factor of different risk monitoring points by the product of the safety risk impact coefficient, the safety risk progress impact coefficient and the monitoring reliability coefficient;

[0098] The safety monitoring risk coefficient of the construction site under construction is determined by using the sum of the safety risk monitoring factors of different risk monitoring points.

[0099] It should be noted that the safety risk progress impact coefficients of different risk monitoring points are determined based on the average daily construction volume of different risk monitoring points, including:

[0100] The safety risk progress impact coefficient of the risk monitoring point is determined based on the average daily construction engineering volume of the risk monitoring point and a preset mapping function.

[0101] Furthermore, when the safety monitoring risk coefficient of the construction site under construction is not within a preset risk coefficient range, the construction site under construction is determined to be a risky construction site.

[0102] It should be further explained that the method for determining the safety monitoring risk coefficient of the construction site under construction is:

[0103] S31 obtains the number of risk monitoring points of the construction site under construction, and determines whether the number of risk monitoring points of the construction site under construction is less than the preset number of risk points. If so, proceeds to the next step; if not, proceeds to step S36;

[0104] S32 determines the average daily construction volume of different risk monitoring points based on the construction data of the risk monitoring points of the construction site under construction, and judges whether the sum of the average daily construction volume of the risk monitoring points meets the requirements. If yes, proceed to the next step; if not, proceed to step S34;

[0105] S33 determines whether the average daily number of construction workers at the risk monitoring point meets the requirements. If yes, it is determined that the construction site under construction does not belong to the risky construction site. If not, proceed to the next step.

[0106] S34 determines the safety risk progress impact coefficient of different risk monitoring points by the average daily construction project volume of different risk monitoring points, and determines the safety risk personnel impact coefficient of different risk monitoring points by the average daily number of construction personnel at different risk monitoring points;

[0107] S35 determines the safety risk monitoring factors of different risk monitoring points by multiplying the safety risk impact coefficient, the safety risk progress impact coefficient and the monitoring reliability coefficient, and judges whether there are risk monitoring points whose safety risk monitoring factors do not meet the requirements. If so, proceeds to the next step; if not, determines that the construction site under construction does not belong to a risk construction site;

[0108] S36 determines the safety monitoring risk coefficient of the construction site under construction by using the sum of the safety risk monitoring factors of different risk monitoring points.

[0109] S4 obtains the safety risk points of the risky construction site and the construction data of different safety risk points, and determines the on-site safety inspection strategy of the risky construction site by using a pre-trained AI model in combination with the safety monitoring risk coefficient of the risky construction site.

[0110] Specifically, the method for determining the on-site safety inspection strategy of the risky construction site is:

[0111] Determine the average daily number of construction workers at different safety risk points based on the construction data at different safety risk points;

[0112] Taking the average daily number of construction workers at different safety risk points, the number of safety risk points at risky construction sites, and the safety monitoring risk coefficient of risky construction sites as input, a pre-trained AI model is used to determine the matching coefficient between the risky construction sites and different on-site safety inspection strategies;

[0113] An on-site safety inspection strategy for the risky construction site is determined based on the matching coefficient.

[0114] Furthermore, the on-site safety inspection strategy with the largest matching coefficient is used as the on-site safety inspection strategy for the risky construction site.

[0115] It is understandable that the on-site safety inspection strategy for the risky construction site includes an inspection cycle for the on-site safety inspection.

[0116] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0117] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A construction enterprise management method based on artificial intelligence, characterized in that: Specifically include: Obtain the distribution of the construction sites under construction of the construction enterprise, and when it is determined that the difficulty of the on-site safety inspection of the construction enterprise does not meet the requirements in combination with the number of the construction sites under construction and the safety risk point data of the construction enterprise, proceed to the next step; Determine the setting data of the Internet of Things monitoring devices in the safety risk points of different construction sites under construction, and determine the monitoring reliability coefficients of different safety risk points in combination with the analysis results of the historical monitoring data of the different Internet of Things monitoring devices, and use the monitoring reliability coefficients to determine the risk monitoring points of the construction sites under construction; Based on the construction data of risk monitoring points and monitoring reliability coefficients of different construction sites under construction, the safety monitoring risk coefficients of different construction sites under construction and the determination of risky construction sites are carried out; Obtain the safety risk points of the risky construction site and the construction data of different safety risk points, and determine the on-site safety inspection strategy of the risky construction site by using a pre-trained AI model in combination with the safety monitoring risk coefficient of the risky construction site; It is determined that the difficulty of on-site safety inspection of the construction enterprise does not meet the requirements, including: Based on the distribution of construction sites under construction, the locations of different construction sites under construction are determined, and the interval distances between different construction sites under construction are determined based on the locations of different construction sites under construction, and the distance difficulty coefficient of the on-site safety inspection of the construction enterprise is determined using the interval distances; Determine the number of different types of safety risk points at different construction sites through the safety risk point data at different construction sites, and determine the risk inspection difficulty coefficients at different construction sites using the number of different types of safety risk points; Determine the comprehensive risk inspection difficulty coefficient of the construction enterprise based on the risk inspection difficulty coefficients of different construction sites under construction and the number of construction sites under construction, and determine the on-site inspection difficulty coefficient of the construction enterprise in combination with the distance difficulty coefficient of the on-site safety inspection of the construction enterprise, and determine whether the on-site safety inspection difficulty of the construction enterprise meets the requirements based on the on-site inspection difficulty coefficient; The method for determining the monitoring reliability coefficient of the safety risk point is: Determine the setting data of different types of Internet of Things monitoring devices at different safety risk points based on the setting data of the Internet of Things monitoring devices at the safety risk points of the construction site under construction, and use them as risk point monitoring devices; Determine the number of abnormal monitoring periods of different risk point monitoring devices through the analysis results of the historical monitoring data of the risk point monitoring device, and determine the monitoring risk coefficients of different risk point monitoring devices in combination with the number of abnormal data in different abnormal monitoring periods; Determine the reliability coefficient evaluation amount of different types of risk point monitoring devices based on the number of different types of risk point monitoring devices and the monitoring risk coefficients of the risk point monitoring devices, and determine the monitoring reliability coefficient of the safety risk point with the preset weight coefficients and reliability coefficient evaluation amounts of the different types of risk point monitoring devices; The method for determining the safety monitoring risk coefficient of the construction site under construction is: Determine the average daily construction volume of different risk monitoring points based on the construction data of the risk monitoring points of the construction site under construction, and determine the safety risk progress impact coefficient of different risk monitoring points based on the average daily construction volume of different risk monitoring points; Determine the safety risk personnel impact coefficient of different risk monitoring points by the average daily number of construction personnel at different risk monitoring points, and determine the safety risk monitoring factor of different risk monitoring points by the product of the safety risk personnel impact coefficient, the safety risk progress impact coefficient and the monitoring reliability coefficient; The safety monitoring risk coefficient of the construction site under construction is determined by using the sum of the safety risk monitoring factors of different risk monitoring points.

2. The construction enterprise management method based on artificial intelligence according to claim 1, characterized in that: The distribution of the construction sites under construction includes the locations of different construction sites under construction.

3. The construction enterprise management method based on artificial intelligence according to claim 1, characterized in that: The safety risk point data includes the number of different types of safety risk points, which is specifically determined according to the type of the construction site under construction.

4. The construction enterprise management method based on artificial intelligence according to claim 1, characterized in that: When the difficulty of the on-site safety inspection of the construction enterprise meets the requirements, on-site safety inspections of different construction sites under construction are carried out according to preset cycles.

5. The construction enterprise management method based on artificial intelligence according to claim 1, characterized in that: The setting data of the Internet of Things monitoring device includes the setting quantity of different types of Internet of Things monitoring devices.

6. The construction enterprise management method based on artificial intelligence according to claim 1, characterized in that: The analysis result of the historical monitoring data includes the number of abnormal monitoring periods and the number of data anomalies in different abnormal monitoring periods.

7. The construction enterprise management method based on artificial intelligence according to claim 1, characterized in that: The monitoring reliability coefficient of the safety risk point has a value range between 0 and 1, wherein when the monitoring reliability coefficient of the safety risk point is not within a preset reliability coefficient interval, the safety risk point is determined to be a risk monitoring point.

8. The construction enterprise management method based on artificial intelligence according to claim 1, characterized in that: The method for determining the on-site safety inspection strategy of the risky construction site is: Determine the average daily number of construction workers at different safety risk points based on the construction data at different safety risk points; Taking the average daily number of construction workers at different safety risk points, the number of safety risk points at risky construction sites, and the safety monitoring risk coefficient of risky construction sites as input, a pre-trained AI model is used to determine the matching coefficient between the risky construction sites and different on-site safety inspection strategies; An on-site safety inspection strategy for the risky construction site is determined based on the matching coefficient.

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