Engineering construction whole process digital supervision method and system

Abstract

The invention provides an engineering construction whole process digital supervision method and system, and relates to the field of engineering construction supervision, and the method comprises the steps: determining the installation position of a sensor used for monitoring an environment based on an engineering project plan and an engineering construction region map; acquiring environment data acquired by the sensor at each mounting position in real time; inputting the environment data into an environment prediction model in a time sequence format, and outputting an environment prediction result in a future preset time period; determining a risk assessment value corresponding to each piece of environment data in a future preset time period according to the environment prediction result; and selecting a corresponding construction adjustment plan according to the risk assessment value and pushing the plan to a display end. By implementing the method, the user can be helped to know the potential environmental risk in advance so as to make adjustments or take preventive measures in advance.

Description

Technical Field

[0001] The present application relates to the field of engineering construction supervision, and in particular to a method and system for digital supervision of the entire process of engineering construction. Background Art

[0002] In the field of engineering construction, it is crucial to ensure that the environment of the construction site is safe and meets regulatory requirements. With the acceleration of urbanization and industrialization, the impact of engineering projects on the environment has received increasing attention. Therefore, it is particularly important to effectively monitor environmental factors such as air quality, noise and water quality during the construction process.

[0003] In related technologies, engineering projects usually use fixed environmental monitoring stations or portable sensors to monitor the environmental conditions of the construction site. These devices can collect data such as air quality, noise level and water quality parameters in real time and transmit the data to the central monitoring system. Monitoring personnel can conduct daily supervision based on real-time data and take necessary countermeasures to ensure that construction activities do not cause unacceptable impacts on the surrounding environment.

[0004] However, when environmental conditions are detected to be deteriorating based on real-time environmental data, damage to the environment has often already occurred, making it difficult to adjust construction plans in a timely manner, which may lead to violations of environmental regulations or impacts on public health. Summary of the invention

[0005] The present application provides a method and system for digitally supervising the entire process of engineering construction, which is used to solve the problem that it is difficult to adjust the construction strategy in time when environmental conditions are detected to have deteriorated.

[0006] In a first aspect, the present application provides a method for digital supervision of the entire process of engineering construction, which is applied to a digital supervision system, and the method includes: Determine the installation location of sensors for monitoring the environment based on the project plan and the construction area map, including air quality sensors, noise sensors and water quality monitoring sensors; Real-time access to environmental data collected by sensors at each installation location, including air quality index, noise decibel level, and water quality level; The environmental data is input into an environmental prediction model in a time series format, and an environmental prediction result within a preset time period in the future is output. The environmental prediction model is trained by historical environmental data, and the environmental prediction result includes an air quality index prediction value, a noise decibel level prediction value, and a water quality grade prediction value; Determine the risk assessment value corresponding to each environmental data in a future preset time period based on the environmental prediction result; According to the risk assessment value, the corresponding construction adjustment plan is selected and pushed to the display end.

[0007] Through the above embodiments, the digital monitoring system installs sensors at specific locations in the construction area to monitor environmental conditions and predict environmental conditions in the future based on the monitoring values, which can help users learn about potential environmental risks in advance and make adjustments or take preventive measures in advance.

[0008] In some embodiments, before the step of inputting the environmental data in a time series format into the environmental prediction model and outputting the environmental prediction result within a future preset time period, the step further includes: Construct time series data sets corresponding to different environmental data types based on historical environmental data and corresponding meteorological data, which include temperature, humidity, and wind speed; The time series dataset is used to train the deep learning models separately to obtain multiple environmental prediction models.

[0009] Through the above embodiments, the digital supervision system introduces meteorological data and historical environmental data to construct a time series data set to train the deep learning model. The resulting multiple environmental prediction models can more accurately reflect the impact of different environmental parameters on future environmental conditions, making the prediction results more targeted.

[0010] In some embodiments, the step of inputting the environmental data into the environmental prediction model in a time series format and outputting the environmental prediction results within a future preset time period specifically includes: Obtain the forecast value of meteorological data within a preset time period in the future through the weather forecast platform; Generate multiple time series data according to the environmental data and the predicted values ​​of the meteorological data; The time series data are respectively input into the corresponding environmental prediction models to obtain environmental prediction results.

[0011] Through the above embodiments, the digital supervision system obtains the predicted values ​​of future meteorological data, combines them with the current environmental data, generates multiple time series data and inputs them into the corresponding environmental prediction model to predict future environmental data, taking into account the impact of meteorological data on environmental data and improving the accuracy of model prediction.

[0012] In some embodiments, after the step of inputting the time series data into the corresponding environment prediction model to obtain the environment prediction result, the following step is further included: If it is detected that there is data exceeding the corresponding preset threshold in the environmental prediction result, a corresponding information warning will be issued.

[0013] Through the above embodiments, when the environmental prediction results show that certain data exceeds the preset threshold, the digital supervision system automatically issues an early warning, which can help construction management personnel to timely understand potential high-risk environmental conditions and take countermeasures to effectively avoid or reduce the negative impact caused by environmental factors.

[0014] In some embodiments, after the step of acquiring the environmental data collected by the sensor at each installation location in real time, the method further includes: Generating a plurality of change curves according to the environmental data, the plurality of change curves respectively corresponding to an air quality index, a noise decibel level, and a water quality grade; Determine a change trend of each environmental data according to the multiple change curves, the change trend including an upward trend, a stable trend and a downward trend; The frequency with which the sensor collects the environmental data is adjusted according to the change trend.

[0015] Through the above embodiments, the digital monitoring system can timely adjust the collection frequency of the sensor by monitoring the changing trends of air quality, noise and water quality, optimize the data collection process, and enhance the flexibility and efficiency of data collection.

[0016] In some embodiments, before the step of selecting a corresponding construction adjustment plan according to the risk assessment value and pushing it to the display end, the step further includes: Obtain different risk assessment value ranges and corresponding construction adjustment plans.

[0017] Through the above embodiment, before selecting the adjustment plan to be pushed to the display end, the digital supervision system first obtains different risk assessment value ranges and corresponding construction adjustment plans, making the construction adjustment more targeted and timely, and ensuring the continuity of the construction process and environmental safety.

[0018] In some embodiments, after the step of selecting a corresponding construction adjustment plan according to the risk assessment value and pushing it to the display end, the method further includes: After executing the construction adjustment plan, a new change curve corresponding to the new environmental data is generated; Whether the construction adjustment plan is effective is determined based on the new change curve.

[0019] Through the above-mentioned embodiments, the digital supervision system generates a new change curve corresponding to the new environmental data after the construction adjustment plan is executed, and compares it with the original change curve to determine the effectiveness of the adjustment plan, thereby achieving real-time optimization of the construction process.

[0020] In a second aspect, the present application provides a digital supervision system, the digital supervision system comprising: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the digital supervision system can implement a digital supervision method for the entire process of engineering construction provided in the above-mentioned embodiment, which will not be repeated here.

[0021] On the third aspect, the present application provides a computer-readable storage medium, including instructions. When the instructions are executed on a digital supervision system, the digital supervision system can implement a method for digital supervision of the entire process of engineering construction provided in the above-mentioned embodiment, which will not be repeated here.

[0022] Fourthly, the present application provides a computer program product. When the computer program product runs on a digital supervision system, the digital supervision system can implement a method for digital supervision of the entire process of engineering construction provided in the above-mentioned embodiment, which will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By integrating meteorological data and historical environmental data to build time series data sets, and using these data sets to train multiple deep learning models, environmental prediction models for different types of environmental data are generated to predict future environmental conditions, thereby improving the accuracy of model predictions. At the same time, it can help users learn about potential environmental risks in advance, so as to make adjustments or take preventive measures in advance.

[0024] 2. The real-time early warning mechanism can automatically issue an alarm when the environmental prediction results exceed the preset threshold, improving the response speed to potential high-risk environmental conditions.

[0025] 3. Not only can the most appropriate construction adjustment plan be selected based on real-time monitoring and forecast data, but the effectiveness of the plan can also be evaluated through subsequent changes in environmental data. This enables construction adjustments to be optimized in real time based on actual environmental changes, improving the flexibility and efficiency of the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a method for digital supervision of the entire process of engineering construction in an embodiment of the present application; Figure 2 This is another flow chart of a method for digital supervision of the entire process of engineering construction in an embodiment of the present application; Figure 3 It is a flow chart of the digital supervision system in the embodiment of the present application performing feedback adjustment on the data collection frequency and the construction adjustment plan according to the change curve of the environmental data; Figure 4It is a schematic diagram of the structure of a physical device of the digital supervision system in the embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the following is a flow chart of the method provided in this implementation. Figure 1 The figure is a flow chart of a method for digital supervision of the entire process of engineering construction in an embodiment of the present application.

[0030] S101. Determine installation locations of sensors for monitoring the environment based on the engineering project plan and the engineering construction area map.

[0031] The project plan can be a structured digital document, such as an XML file or JSON format, which contains the key parameters and attributes of the project. The construction area map can be a digital two-dimensional vector map or a three-dimensional model file, such as AutoCAD's DWG / DXF format, Shapefile format, or CityGML format. The sensor installation location can be represented by spatial coordinates, such as latitude and longitude coordinates, plane coordinates, or three-dimensional coordinates.

[0032] Specifically, the digital supervision system reads the project planning documents, parses the key information therein, and extracts parameters that affect the sensor layout, such as the construction area scope, building location, construction phase arrangement, etc.; secondly, the digital supervision system loads the project construction area map file and converts it into a computer-processable data structure, such as a two-dimensional array, a three-dimensional point cloud, or a geographic information system (GIS) spatial database; the system then calculates and generates a set of optimal sensor installation location coordinates based on the preset sensor layout algorithm (i.e., the installation location selection rules for air quality sensors, noise sensors, and water quality monitoring sensors), combined with the project planning parameters and map data.

[0033] In some embodiments, sensor layout optimization can be achieved through a variety of computer algorithms: Optionally, a rule-based layout method is used to divide the construction area into several grid units, and the optimal sensor configuration of each unit is inferred using decision trees or expert system rules based on the attribute characteristics of each unit (such as functional area type, terrain undulation, distance from sensitive points, etc.); then the layout results of all units are integrated to form a complete sensor layout plan. Optionally, a random search optimization algorithm, such as particle swarm optimization (PSO) and genetic algorithm (GA), is used to take the sensor coordinates as the decision variables to be optimized, and indicators such as coverage area, monitoring accuracy, and cost are constructed into an objective function, and the global optimal solution is found through repeated iterative searches.

[0034] Of course, in some embodiments, the installation positions of the air quality sensor, the noise sensor, and the water quality monitoring sensor may also be directly set by the user, which is not limited here.

[0035] S102: Acquire environmental data collected by sensors at each installation location in real time.

[0036] The sensors installed at each installation location are different. The installed sensors may be one or more of air quality sensors, noise sensors, and water quality monitoring sensors. The specific type of sensor installed needs to be determined based on actual needs. The specific matching rules have been introduced in step S101 and will not be repeated here.

[0037] Air quality sensors, noise sensors, and water quality monitoring sensors deployed at various monitoring points on the construction site upload measurement data in real time to the data receiving server of the digital supervision system through wireless communication modules (such as GPRS, NB-IoT, LoRa, etc.). The data receiving server performs pre-processing operations such as parsing, verification, filtering, and normalization on these time-varying environmental monitoring data streams, and stores the structured time series data in a time series database.

[0038] S103: Input the environmental data into the environmental prediction model in a time series format, and output the environmental prediction results within a preset time period in the future.

[0039] Among them, the time series format refers to a data sequence formed by arranging environmental data at different times in chronological order, and each data point has a timestamp attribute.

[0040] Specifically, the digital supervision system extracts environmental data within a certain time range (such as the last month) from the time series database, including air quality index (AQI), noise decibel level (dB), water quality level, etc., and generates corresponding time series data sets based on each type of environmental data. These time series reflect the dynamic characteristics of environmental parameters changing over time.

[0041] Next, the digital supervision system slices the time series data set according to a certain time window (such as hours, days), and converts the data in each time segment into a feature vector. These feature vectors are then input into the pre-trained environmental prediction model to obtain the environmental prediction results (i.e., the predicted value of the air quality index, the predicted value of the noise decibel level, and the predicted value of the water quality grade) for the future preset time period.

[0042] It should be noted that the environmental prediction model can use a variety of machine learning algorithms to build a model framework, such as long short-term memory network (LSTM), gated recurrent unit network (GRU), temporal convolutional network (TCN), etc., to capture the long-term dependencies and periodic patterns in time series data, thereby realizing the trend prediction of future environmental quality. The choice of specific algorithms needs to be determined according to actual needs and is not limited here.

[0043] S104: Determine the risk assessment value corresponding to each environmental data in a future preset time period based on the environmental prediction result.

[0044] The digital supervision system sets risk assessment matrices for different environmental factors such as air quality index, noise decibel level, water quality level, etc. The horizontal axis of the matrix represents the numerical level of the monitoring indicator (such as excellent, good, light pollution, moderate pollution, etc.), and the vertical axis represents the predicted probability of occurrence (such as very likely, more likely, possible, less likely, etc.). Each cell in the matrix corresponds to a risk assessment value, which can be represented by a characteristic color or number (such as red / 4 for high risk, yellow / 3 for relatively high risk, blue / 2 for medium risk, and green / 1 for low risk).

[0045] Next, the digital supervision system matches the environmental prediction results of each monitoring point with the corresponding risk matrix to determine which level range the predicted index value belongs to. At the same time, combined with the probability of the prediction occurring, the corresponding risk assessment value is found in the matrix.

[0046] For example, in a specific embodiment, the predicted sound level value of a noise monitoring point in the next 8 hours is 70 decibels. According to the noise risk matrix, this value belongs to the "slightly exceeded" level, and the predicted probability is "more likely" to occur. Therefore, the risk assessment value corresponding to the noise at this point is 3 (higher risk).

[0047] S105. Select a corresponding construction adjustment plan based on the risk assessment value and push it to the display end.

[0048] The digital supervision system has prepared a corresponding construction adjustment plan library for different risk levels in advance. For example, the noise risk plan library may include "medium noise operation suspension order", "overtime construction restriction order", "equipment update / maintenance notice", etc.; the air pollution risk plan library may include "road watering frequency increase order", "dust-prone material covering order", "sludge truck washing order", etc. These plans are usually jointly formulated by construction management experts and environmental protection experts and stored in the database of the digital supervision system. .

[0049] For example, in some embodiments, when the risk assessment value of a monitoring point reaches a preset trigger condition (such as high risk lasting for more than 2 hours), the digital supervision system automatically matches the construction adjustment plan corresponding to the risk level, generates a structured instruction message, and sends it to the relevant responsible person through a message push service (such as SMS, WeChat public account, mobile app, etc.). At the same time, the digital supervision system will also display the prompt information of the plan adjustment in real time in a striking visual way (such as pop-up prompts, map marks, warning lights, etc.) on the supervision cockpit interface of the intelligent control platform and mobile terminal, guiding managers to quickly view and execute adjustment instructions.

[0050] In the above-mentioned embodiment, the digital monitoring system installs sensors at specific locations in the construction area to monitor environmental conditions and predict environmental conditions in the future based on the monitoring values, which can help users learn about potential environmental risks in advance and make adjustments or take preventive measures in advance.

[0051] The following is a more detailed description of the process of the method provided by this embodiment. Figure 2 As shown, it is another flow chart of a method for digital supervision of the entire process of engineering construction in an embodiment of the present application.

[0052] S201. Construct time series data sets corresponding to different environmental data types based on historical environmental data and corresponding meteorological data.

[0053] The digital supervision system needs to perform preprocessing operations such as cleaning, alignment, and interpolation on the original environmental and meteorological time series data. The processed environmental and meteorological time series data are divided according to type (such as air quality index, noise decibel level, and water quality grade) and constructed into three time series data sets. For example, the air quality index in the environmental time series data and the corresponding meteorological time series data are used to construct a time series data set; the noise decibel level in the environmental time series data and the corresponding meteorological time series data are used to construct a time series data set; the water quality grade in the environmental time series data and the corresponding meteorological time series data are used to construct a time series data set.

[0054] S202. Use the time series data set to train the deep learning models respectively to obtain multiple environmental prediction models.

[0055] The digital supervision system uses the multiple time series data sets constructed in step S201 to train the corresponding deep learning model, thereby obtaining the environmental prediction model corresponding to different environmental variables.

[0056] Specifically, for time series data sets, the digital supervision system builds an independent deep learning model. Given the input environmental data and meteorological time series characteristics (i.e., time series data sets), the deep learning model minimizes the error between the predicted output and the actual environmental measurement value by adjusting the internal neuron connection weights. The resulting environmental prediction model can automatically predict the environmental data for a period of time in the future based on the input historical environmental data and the corresponding meteorological data.

[0057] For example, in a specific embodiment, for the task of predicting the air quality index at the construction site, the digital supervision system selects an LSTM model. The input features are the time series data of AQI (air quality index), temperature, humidity, and wind speed in the past 72 hours, and the output target is the AQI forecast value for the next 24 hours. The original data for 6 months is divided into three parts: training set, validation set, and test set, accounting for 70%, 20%, and 10% respectively. The LSTM model is parameter-learned on the training set, and the validation set is used to monitor the training progress, and hyperparameters are tuned according to the model performance. Finally, the trained model is evaluated on the test set, and various prediction accuracy indicators such as RMSE, MAE, R2, etc. are calculated. If the model performance meets the actual application requirements, it is saved as a dedicated model for AQI prediction at the construction site, that is, the environmental prediction model corresponding to the air quality index.

[0058] In the above embodiment, the digital supervision system constructs a time series data set by introducing meteorological data and historical environmental data to train the deep learning model. The resulting multiple environmental prediction models can more accurately reflect the impact of different environmental parameters on future environmental conditions, making the prediction results more targeted.

[0059] S203: Obtain forecast values ​​of meteorological data within a preset time period in the future through a weather forecast platform.

[0060] Specifically, the digital supervision system can read the future weather information released by the weather forecast platform as the forecast value of meteorological data within a preset time period in the future, and use the forecast value as the input of the environmental prediction model.

[0061] S204: Generate multiple time series data based on the environmental data and the predicted values ​​of the meteorological data.

[0062] The digital supervision system aligns the current environmental data with the weather forecast data for the future period and combines the values ​​to generate the time series input format required by the environmental prediction model. Each environmental data, such as air quality index, noise decibel level and water quality level, corresponds to a time series data.

[0063] S205: Input the time series data into corresponding environmental prediction models to obtain environmental prediction results.

[0064] The digital supervision system inputs the multiple time series data obtained in step S204 into the multiple environmental prediction models obtained in step S202, for example, inputs the time series data corresponding to the air quality index into the environmental prediction model corresponding to the air quality index, and obtains the predicted value of the air quality index within the future preset time. Similarly, the predicted value of the noise decibel level and the predicted value of the water quality grade can be obtained. The digital supervision system then combines the predicted value of the air quality index, the predicted value of the noise decibel level and the predicted value of the water quality grade to obtain the environmental prediction result.

[0065] In the above embodiment, the digital supervision system obtains the predicted value of future meteorological data, combines it with the current environmental data, generates multiple time series data and inputs them into the corresponding environmental prediction model to predict the future environmental data, taking into account the impact of meteorological data on environmental data and improving the accuracy of model prediction.

[0066] S206: If it is detected that there is data exceeding the corresponding preset threshold in the environmental prediction result, a corresponding information warning is issued.

[0067] After completing the risk assessment and matching the plan, the digital supervision system will also determine whether there is an emergency situation that needs to be dealt with immediately. If certain indicator values ​​are detected in the environmental prediction results and have exceeded the pre-set warning threshold, an alert will be immediately issued to relevant personnel through the information warning mechanism.

[0068] In the above embodiment, when the environmental prediction results show that certain data exceeds the preset threshold, the digital supervision system automatically issues an early warning, which can help construction management personnel to promptly understand potential high-risk environmental conditions and take countermeasures to effectively avoid or mitigate the negative impacts caused by environmental factors.

[0069] The following is a flow chart of a digital monitoring system in an embodiment of the present application that performs feedback adjustment on the data collection frequency and the construction adjustment plan according to the change curve of the environmental data. Figure 3 shown.

[0070] S301. Acquire environmental data collected by sensors at each installation location in real time.

[0071] The digital monitoring system continuously obtains the latest environmental data collected by sensors installed at various monitoring points through real-time communication. These sensors include air quality sensors, noise sensors, and water quality monitoring sensors, which are distributed in different locations in the construction area and cover environmentally sensitive areas that need to be monitored.

[0072] S302: Generate multiple change curves according to environmental data.

[0073] After acquiring the real-time data stream collected by the environmental sensors, the digital monitoring system converts the time series data of each monitoring indicator into a line graph. For example, the decibel value reported by the noise sensor every minute is plotted on a two-dimensional coordinate graph with time as the horizontal axis and noise intensity as the vertical axis. By connecting the data points at adjacent time points, a curve reflecting the dynamic changes in the noise level is obtained. Similarly, the change curves corresponding to the noise decibel level and water quality level can be obtained respectively.

[0074] S303: Determine a change trend of each environmental data according to a plurality of change curves.

[0075] The digital supervision system arranges the environmental monitoring data within a certain time range in chronological order to form a complete time series. Then, the sequence is smoothed using techniques such as moving average and exponential smoothing to remove the interference of random fluctuations and outliers, and to extract the trend components that reflect the overall direction of change. On this basis, curve fitting methods such as least squares method and spline interpolation are used to construct a mathematical model for the trend sequence of each monitoring indicator to obtain a smooth fitting curve.

[0076] By analyzing the characteristic values ​​of the slope and curvature of the fitting curve, the system can automatically determine whether the indicator is in an upward trend, a downward trend, or a relatively stable state. For example, if the slope of the fitting curve is positive and gradually increases in the recent period of time, it is determined that the indicator is showing an accelerating upward trend; conversely, if the slope is negative and gradually decreases, it is determined to be showing an accelerating downward trend; if the slope remains near 0 for a long time, it is determined to be showing a stable trend.

[0077] S304: Adjust the frequency of collecting environmental data by the sensor according to the change trend.

[0078] The digital supervision system has a set of rules engine in the background that adjusts the data sampling frequency according to the trend of indicator changes. When an indicator is judged to be in a stable state, the rule engine will issue an instruction to appropriately extend the sampling period of the corresponding sensor; when the indicator shows a clear upward or downward trend, the sampling period will be shortened.

[0079] In the above embodiment, the digital monitoring system can timely adjust the sensor collection frequency and optimize the data collection process by monitoring the changing trends of air quality, noise and water quality, thereby enhancing the flexibility and efficiency of data collection.

[0080] S305: After executing the construction adjustment plan, a new change curve corresponding to the new environmental data is generated.

[0081] After the digital supervision system pushes construction adjustment plans to construction management personnel based on environmental prediction models and risk assessment results, it will continue to track the actual effects of the adjustment measures after implementation and evaluate their effectiveness in improving the environment.

[0082] Specifically, after executing the construction adjustment plan, the digital supervision system continuously obtains new environmental data collected by sensors and generates a new change curve based on the new environmental data. The generation process of the new change curve is the same as step S302 and will not be repeated here.

[0083] S306. Determine whether the construction adjustment plan is effective based on the new change curve.

[0084] After the construction adjustment plan is implemented, the digital supervision system continues to obtain new environmental monitoring data and draws a curve chart reflecting the latest changes based on these new data. By comparing and analyzing the new and old change curves, the system can intuitively evaluate the actual effect of the adjustment plan and determine whether it has effectively improved environmental problems.

[0085] For example, in one embodiment, if after the implementation of the adjustment plan, the curve begins to show a positive development trend, such as a slowing upward trend, a faster downward trend, or a reduced fluctuation, it preliminarily indicates that the adjustment measures are taking effect, that is, the construction adjustment plan is effective. On the contrary, if the curve change trend is basically the same, it is determined that the construction adjustment plan is invalid.

[0086] In the above embodiment, the digital supervision system generates a new change curve corresponding to the new environmental data after the construction adjustment plan is executed, and compares it with the original change curve to determine the effectiveness of the adjustment plan, thereby achieving real-time optimization of the construction process.

[0087] The digital monitoring system of the embodiment of the present invention is applied to electronic equipment. Figure 4 A schematic diagram of the architecture of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0088] It should be noted that Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0089] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling related hardware through instructions (computer programs), and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein a plurality of instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided in the embodiment of the present invention.

[0090] Specifically, the storage medium and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more signal lines. The storage medium stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, random access storage medium (Random Access Memory, referred to as: RAM), read-only storage medium (Read Only Memory, referred to as: ROM), programmable read-only storage medium (Programmable Read-Only Memory, referred to as: PROM), erasable read-only storage medium (Erasable Programmable Read-Only Memory, referred to as: EPROM), electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, referred to as: EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the program after receiving the execution instruction.

[0091] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps, and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0092] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0093] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A digital supervision method for the entire process of engineering construction, applied to a digital supervision system, characterized in that: The method comprises: Determine the installation location of sensors for monitoring the environment based on the project plan and the construction area map, wherein the sensors include air quality sensors, noise sensors and water quality monitoring sensors; Real-time acquisition of environmental data collected by sensors at each installation location, including air quality index, noise decibel level, and water quality grade; Input the environmental data into an environmental prediction model in a time series format, and output environmental prediction results within a preset time period in the future, wherein the environmental prediction model is trained by historical environmental data, and the environmental prediction results include air quality index prediction values, noise decibel level prediction values, and water quality grade prediction values; Determine the risk assessment value corresponding to each environmental data in a future preset time period according to the environmental prediction result; According to the risk assessment value, a corresponding construction adjustment plan is selected and pushed to the display end.

2. The method according to claim 1, characterized in that Before the step of inputting the environmental data into the environmental prediction model in a time series format and outputting the environmental prediction results within a future preset time period, the step further includes: Constructing time series data sets corresponding to different environmental data types based on historical environmental data and corresponding meteorological data, wherein the meteorological data includes temperature, humidity and wind speed; The time series data sets are used to train the deep learning models respectively to obtain multiple environmental prediction models.

3. The method according to claim 1, characterized in that The step of inputting the environmental data into the environmental prediction model in a time series format and outputting the environmental prediction results within a future preset time period specifically includes: Obtain the forecast value of meteorological data within a preset time period in the future through the weather forecast platform; Generate multiple time series data according to the environmental data and the predicted values ​​of the meteorological data; The time series data are respectively input into corresponding environmental prediction models to obtain environmental prediction results.

4. The method according to claim 3, characterized in that After the step of inputting the time series data into the corresponding environmental prediction model to obtain the environmental prediction result, the method further includes: If it is detected that there is data exceeding the corresponding preset threshold in the environmental prediction result, a corresponding information warning is issued.

5. The method according to claim 1, characterized in that After the step of acquiring the environmental data collected by the sensor at each installation location in real time, the method further includes: Generating a plurality of change curves according to the environmental data, wherein the plurality of change curves respectively correspond to an air quality index, a noise decibel level, and a water quality grade; Determine a change trend of each environmental data according to the multiple change curves, wherein the change trend includes an upward trend, a stable trend and a downward trend; The frequency with which the sensor collects the environmental data is adjusted according to the change trend.

6. The method according to claim 1, characterized in that Before the step of selecting a corresponding construction adjustment plan according to the risk assessment value and pushing it to the display end, the method further includes: Obtain different risk assessment value ranges and corresponding construction adjustment plans.

7. The method according to claim 1, characterized in that After the step of selecting the corresponding construction adjustment plan according to the risk assessment value and pushing it to the display end, the method further includes: After executing the construction adjustment plan, a new change curve corresponding to the new environmental data is generated; Whether the construction adjustment plan is effective is determined based on the new change curve.

8. A digital supervision system, characterized in that: The digital supervision system includes: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, wherein the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the digital supervision system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the digital supervision system, the digital supervision system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a digital supervision system, the digital supervision system is enabled to execute the method according to any one of claims 1 to 7.

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