Ultrahigh corbel column construction state real-time monitoring and regulation method based on Internet of Things

Abstract

The invention discloses an ultrahigh bracket column construction state real-time monitoring and regulation method based on the Internet of Things, and relates to the field of constructional engineering, and the method comprises the steps: S1, obtaining construction state data through an Internet of Things server, and importing a detection algorithm after preprocessing; s2, the detection algorithm identifies abnormal data and imports the abnormal data into an evaluation algorithm, wherein the abnormal data are data points exceeding a set range or abnormally fluctuating; and S3, the evaluation algorithm evaluates the construction state in real time and generates a regulation and control instruction, and the regulation and control instruction is fed back to the front-end automation equipment through the Internet of Things server for parameter adjustment. Compared with the prior art, the system has the advantages that (1) real-time monitoring and regulation are realized; (2) the data accuracy is high; (3) the intelligent analysis capability is strong; and (4) an instruction feedback mechanism is perfect.

Description

Technical Field

[0001] The present invention relates to the field of construction engineering, and in particular to a method for real-time monitoring and regulating the construction status of a super-high corbel column based on the Internet of Things. Background Art

[0002] At present, the construction status monitoring of super-high corbels mainly relies on manual inspections and regular testing, which has problems such as time lag, inaccurate data, and slow response. At the same time, most existing monitoring systems can only provide a single data collection function, lack effective data processing and intelligent analysis, and cannot achieve real-time monitoring and control of the construction status.

[0003] For example, Publication No. CN116188331B proposes a construction project construction status change monitoring method and system. Although the system can collect construction status data, it lacks in-depth processing and intelligent analysis of the data, and cannot effectively identify abnormal data and generate control instructions. In addition, the system also has deficiencies in data transmission and command feedback, and cannot achieve real-time and accurate control. Summary of the invention

[0004] The technical problems to be solved by the present invention are: (1) limited data collection and processing capabilities make it impossible to achieve real-time monitoring of the construction status; (2) lack of intelligent analysis algorithms makes it impossible to effectively identify abnormal data and generate control instructions; (3) imperfect data transmission and instruction feedback mechanisms make it impossible to achieve real-time and accurate control.

[0005] In order to solve the above technical problems, the technical solution provided by the present invention is: a real-time monitoring and control method for the construction status of super-high corbel columns based on the Internet of Things, comprising:

[0006] S1, obtain construction status data through the Internet of Things server, and import it into the detection algorithm after preprocessing;

[0007] S2, the detection algorithm identifies abnormal data and imports it into the evaluation algorithm. Abnormal data refers to data points that exceed the set range or fluctuate abnormally;

[0008] S3. The evaluation algorithm evaluates the construction status in real time and generates control instructions, which are fed back to the front-end automation equipment through the IoT server for parameter adjustment.

[0009] Furthermore, construction status data is uploaded in real time through various sensors deployed at the super-high corbel construction site. The sensors include stress sensors, displacement sensors, temperature sensors, and humidity sensors. All types of sensors are connected to the IoT server through a channel based on the TCP / IP secure transmission protocol.

[0010] Furthermore, in S1, the construction status data preprocessing step specifically includes:

[0011] S1.1. Format conversion;

[0012] S1.2. Select the timestamp, sensor type, measurement value, and unit included in the construction status data, and use regular expressions to parse the values of each field;

[0013] S1.3. Convert the extracted field values to the corresponding data types and map them to a JSON object to complete the conversion;

[0014] S1.4. Verify whether the converted data conforms to the expected format and range. If so, store it in a computer-readable storage medium; otherwise, throw an exception.

[0015] Furthermore, in S2, the specific steps of the detection algorithm are as follows:

[0016] S2.1. Calculate the mean value and standard deviation;

[0017] S2.2. Determine the normal data range according to the 3σ principle;

[0018] S2.3. Traverse the data set, check whether each data point is within the normal data range, and mark the abnormal data in the original data set.

[0019] Furthermore, in S3, the specific steps of the evaluation algorithm are as follows:

[0020] S3.1. Conduct a preliminary classification based on the statistical characteristics of the abnormal data. If the abnormal data is a single data point exceeding the set range, it is judged as "slightly abnormal"; if multiple consecutive data points exceed the set range or show abnormal fluctuations, it is judged as "severely abnormal";

[0021] S3.2. Import the data into the abnormal index formula and quantify the severity of the abnormal data through the weighted summation index formula;

[0022] S3.2. Classify the construction status according to the value of the abnormal index. If the abnormal index < T1, the construction status is "normal"; if T1 ≤ abnormal index < T2, the construction status is "warning"; if the abnormal index ≥ T2, the construction status is "urgent";

[0023] S3.3. Generate corresponding control instructions according to the classification result of the construction status. If the construction status is "normal", no control instructions are generated; if the construction status is "warning", instructions such as "pay attention to monitoring" or "slightly adjust parameters" are generated; if the construction status is "urgent", instructions such as "immediately stop the machine for inspection" or "significantly adjust parameters" are generated.

[0024] Furthermore, in S3, the specific steps of the feedback of the control instructions are as follows:

[0025] S3.4. The control instruction is represented as C, where C includes a specific device ID, a parameter name to be adjusted, and target value information, where C is a string or data structure containing instruction content and parameter adjustment information.

[0026] S3.5, encode and package the control instructions, the processing process is represented by Encoded_C=Encode(C), where Encode is an encoding function, which converts the control instructions into a format suitable for network transmission;

[0027] S3.6, transmitting the encoded control instruction to the IoT server through the network. The transmission process can be expressed as Send(Encoded_C, Server_Address), where Send is a sending function and Server_Address is the address of the IoT server;

[0028] S3.7, the IoT server receives and parses the control instruction, and the parsing process is represented by Decoded_C=Decode(Encoded_C), where Decode is a decoding function, which restores the received encoded instruction Encoded_C to the original control instruction C;

[0029] S3.8, the IoT server parses the control instruction C, and finds the corresponding front-end automation device according to the device ID information in the instruction. The matching process is expressed as Device = Match (C, Device_List), where Match is a matching function and Device_List is a list of front-end automation devices;

[0030] S3.9, the IoT server sends the control instruction to the matched front-end automation device. The sending process is represented by SendToDevice(C, Device), where SendToDevice is a sending function that sends the control instruction C to the matched device Device;

[0031] S3.10, the front-end automation device receives and parses the control instruction, and then adjusts the device parameters according to the instruction content, but the execution process is represented as Execute(C), where Execute is an execution function, which performs corresponding operations on the device according to the parameter adjustment information in the control instruction C;

[0032] S3.11. After executing the control instruction, the front-end automation device can feed back the execution result to the IoT server. The execution process is expressed as Feedback = ExecuteResult (C), where ExecuteResult is a function that returns the execution result of the control instruction C.

[0033] Furthermore, a real-time monitoring and control system for the construction status of super-high corbel columns based on the Internet of Things is also provided, including a data acquisition module, a data processing module, a detection algorithm module, an evaluation algorithm module, an instruction generation and feedback module, and a front-end automation equipment interface module:

[0034] The data acquisition module is responsible for collecting real-time construction status data from stress sensors, displacement sensors, temperature sensors, humidity sensors, etc. deployed at the super-high corbel construction site. The sensors are connected to the IoT server through the TCP / IP secure transmission protocol to form a stable data acquisition network. The sensor data is directly uploaded to the IoT server for subsequent processing.

[0035] The data processing module pre-processes the construction status data, including format conversion, field parsing, data type mapping and data verification. As the data processing center, this module receives the original data and outputs the formatted and verified data. It receives the data from the data acquisition module and passes the results to the detection algorithm module after processing.

[0036] The detection algorithm module identifies abnormal data, including data points that exceed the set range or fluctuate abnormally, implements anomaly detection based on the 3σ principle, receives data from the data processing module, marks abnormal data, and passes it to the evaluation algorithm module;

[0037] The evaluation algorithm module evaluates the construction status according to the abnormal data and generates control instructions. It includes preliminary classification, abnormal index calculation, construction status classification and control instruction generation submodules. It receives data from the detection algorithm module, generates control instructions after evaluation and passes them to the instruction generation and feedback module.

[0038] The instruction generation and feedback module encodes and packages the control instructions and sends them to the IoT server, and then forwards them to the front-end automation equipment. At the same time, it receives feedback on the execution results. It includes instruction encoding, packaging, sending, parsing, matching, and execution result receiving functions, forming a closed-loop interaction with the IoT server and front-end automation equipment to ensure accurate communication and execution of instructions.

[0039] The front-end automation equipment interface module receives the control instructions forwarded by the IoT server, parses and executes them, adjusts the equipment parameters, and serves as the communication interface of the front-end automation equipment. It supports multiple communication protocols to interact with the equipment, exchanges data with the instruction generation and feedback module through the IoT server, and feeds back the execution results after executing the control instructions.

[0040] Furthermore, the data acquisition module and the data processing module upload and receive data through the Internet of Things server to ensure the real-time nature of the data; after the data processing module outputs formatted data, the detection algorithm module directly reads and analyzes it; after the detection algorithm module marks the abnormal data, the evaluation algorithm module uses these data to evaluate the construction status; after the evaluation algorithm module generates control instructions, the instruction generation and feedback module is responsible for encoding, packaging and sending instructions, and the instructions are forwarded to the front-end automation equipment through the Internet of Things server, and feedback on the execution results is received at the same time; the Internet of Things server acts as a transit station to receive, parse and forward instructions, as well as return the execution results.

[0041] Furthermore, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method of any one of claims 1 to 6 is implemented.

[0042] Furthermore, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods of claims 1 to 6 when executing the program.

[0043] Compared with the prior art, the advantages of the present invention are: (1) Real-time monitoring and control: The Internet of Things technology and intelligent algorithms are used to realize real-time monitoring and intelligent control of the construction status of the super-high corbel column, thereby improving construction safety and efficiency; (2) High data accuracy: A variety of sensors and TCP / IP security transmission protocols are used to ensure the accuracy and integrity of the data. At the same time, the data is pre-processed by the data processing module to further improve the accuracy of the data; (3) Strong intelligent analysis capability: The detection algorithm and evaluation algorithm based on the 3σ principle can effectively identify abnormal data and generate control instructions, thereby realizing intelligent analysis of the construction status; (4) Perfect instruction feedback mechanism: The instruction generation and feedback module and the front-end automation equipment interface module form a closed-loop interaction mechanism to ensure the accurate transmission and execution of the control instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a schematic diagram of the steps of a method for real-time monitoring and control of the construction status of an ultra-high corbel column based on the Internet of Things.

[0045] Figure 2 The present invention is a schematic diagram of the composition of a real-time monitoring and control system for the construction status of an ultra-high corbel column based on the Internet of Things. DETAILED DESCRIPTION

[0046] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0047] Embodiment 1

[0048] like Figure 1 and Figure 2 As shown, this embodiment provides a real-time monitoring and control method for the construction status of a super-high corbel column based on the Internet of Things, including:

[0049] S1, obtain construction status data through the Internet of Things server, and import it into the detection algorithm after preprocessing;

[0050] S1.1, format conversion;

[0051] S1.2. Select the timestamp, sensor type, measurement value, and unit contained in the construction status data, and use regular expressions to parse the values ​​of each field. The regular expression is used to match and extract the timestamp, sensor type, measurement value, and unit in the data row. The regular expression parsing code is:

[0052]

[0053] S1.3. Convert the extracted field values ​​into the corresponding data types and map them into the JSON object. The data type conversion formula uses time as an example. Use the strptime function to convert the string timestamp into a datetime object in the specified format:

[0054] Python

[0055] from datetime import datetime

[0056] timestamp_obj=datetime.strptime(timestamp,'%Y-%m-%d%H:%M:%S')

[0057] Then map it to a JSON object containing timestamp, sensor type, measurement value and unit. JSON object mapping code:

[0058]

[0059]

[0060] S1.4. Verify whether the converted data conforms to the expected format and range. If yes, store it in a computer-readable storage medium. Otherwise, throw an exception. Verification code:

[0061] ifnot(min_value<=float(value)<=max_value):

[0062] raise ValueError(f"Value{value}out ofrange[{min_value},{max_value}]")

[0063] When storing, use SQL statements to insert data into the database.

[0064] S2. The detection algorithm identifies abnormal data and imports it into the evaluation algorithm. Abnormal data are data points that exceed the set range or have abnormal fluctuations.

[0065] S2.1. Calculate the mean and standard deviation. The formula for the mean is: μ = ∑(x_i) / n, where μ is the mean, x_i is each value in the dataset, and n is the number of data points. The formula for the standard deviation is: σ = √[∑(x_i - μ)^2 / n]. Here, σ is the standard deviation, x_i is each value in the dataset, μ is the mean, and n is the number of data points. The standard deviation measures the degree of dispersion of the data points, that is, the distribution width of the data points around the mean.

[0066] S2.2. According to the 3σ principle, normal data should be within 3 standard deviations above and below the mean. The normal data range = [μ - 3σ, μ + 3σ]. This range contains approximately 99.7% of the normally distributed data. Data outside this range are considered outliers.

[0067] S2.3. Traverse the dataset and check whether each data point is within the normal data range. If x_i < (μ - 3σ) or x_i > (μ + 3σ), then x_i is abnormal data, and mark the abnormal data in the original dataset.

[0068] S3. The evaluation algorithm evaluates the construction status in real time and generates control instructions. The control instructions are fed back to the front-end automation equipment through the Internet of Things server for parameter adjustment.

[0069] S3.1. Based on the statistical characteristics of the abnormal data, conduct a preliminary classification. If the abnormal data is a single data point exceeding the set range, it is judged as "slightly abnormal". If multiple consecutive data points exceed the set range or have abnormal fluctuations, it is judged as "severely abnormal".

[0070] S3.2. Import the data into the abnormal index formula and quantify the severity of the abnormal data through the weighted sum index formula. The abnormal index (AI) = ∑(w_i * |x_i - μ_i| / σ_i), where x_i is the value of the abnormal data point, μ_i is the normal mean of the corresponding data point, σ_i is the normal standard deviation of the corresponding data point, and w_i is the weight coefficient, which can be adjusted according to the influence degree of the data point on the construction status.

[0071] S3.2. According to the value of the abnormal index, classify the construction status. If the abnormal index < T1, the construction status is "normal"; if T1 ≤ abnormal index < T2, the construction status is "warning"; if the abnormal index ≥ T2, the construction status is "urgent".

[0072] S3.3. Generate corresponding control instructions based on the construction status classification results. If the construction status is "normal", no control instructions are generated; if the construction status is "warning", an instruction of "pay attention to monitoring" or "slightly adjust parameters" is generated; if the construction status is "emergency", an instruction of "immediately stop for inspection" or "largely adjust parameters" is generated.

[0073] S3.4. The control instruction is represented as C, where C includes a specific device ID, a parameter name to be adjusted, and target value information, where C is a string or data structure containing instruction content and parameter adjustment information.

[0074] S3.5, encode and package the control instructions, the processing process is represented by Encoded_C=Encode(C), where Encode is an encoding function, which converts the control instructions into a format suitable for network transmission;

[0075] S3.6, transmitting the encoded control instruction to the IoT server through the network. The transmission process can be expressed as Send(Encoded_C, Server_Address), where Send is a sending function and Server_Address is the address of the IoT server;

[0076] S3.7, the IoT server receives and parses the control instruction, and the parsing process is represented by Decoded_C=Decode(Encoded_C), where Decode is a decoding function, which restores the received encoded instruction Encoded_C to the original control instruction C;

[0077] S3.8, the IoT server parses the control instruction C, and finds the corresponding front-end automation device according to the device ID information in the instruction. The matching process is expressed as Device = Match (C, Device_List), where Match is a matching function and Device_List is a list of front-end automation devices;

[0078] S3.9, the IoT server sends the control instruction to the matched front-end automation device. The sending process is represented by SendToDevice(C, Device), where SendToDevice is a sending function that sends the control instruction C to the matched device Device;

[0079] S3.10, the front-end automation device receives and parses the control instruction, and then adjusts the device parameters according to the instruction content, but the execution process is represented as Execute(C), where Execute is an execution function, which performs corresponding operations on the device according to the parameter adjustment information in the control instruction C;

[0080] S3.11. After executing the control instruction, the front-end automation device can feed back the execution result to the IoT server. The execution process is expressed as Feedback = ExecuteResult (C), where ExecuteResult is a function that returns the execution result of the control instruction C.

[0081] Embodiment 2

[0082] like Figure 1 As shown, this embodiment provides a specific implementation method of a real-time monitoring and control method for the construction status of an ultra-high corbel column based on the Internet of Things:

[0083] 1. Data Collection and Preprocessing

[0084] At the construction site of the super-high corbel column, stress sensors, displacement sensors, temperature sensors and humidity sensors were deployed. These sensors were connected to the IoT server through the TCP / IP secure transmission protocol to upload construction status data in real time.

[0085] After the IoT server receives the data, the data processing module pre-processes the data. First, the format conversion is performed to ensure the uniformity of the data format; then, regular expressions are used to parse out fields such as timestamp, sensor type, measurement value, and unit; then, the field value is converted to the corresponding data type and mapped to the JSON object; finally, the accuracy and completeness of the data are verified, and the qualified data is stored in a computer-readable storage medium for subsequent analysis.

[0086] 2. Abnormal Data Detection

[0087] The detection algorithm module receives the preprocessed data and performs abnormal data detection. First, the mean and standard deviation of the data are calculated; then, according to the 3σ principle, the range of normal data is determined; finally, the data set is traversed to check whether each data point exceeds the normal data range, and the abnormal data is marked in the original data set.

[0088] 3. Construction status assessment and control instruction generation

[0089] The evaluation algorithm module receives the abnormal data marked by the detection algorithm module and conducts construction status evaluation. First, a preliminary classification is performed based on the statistical characteristics of the abnormal data. A single data point that exceeds the set range is judged as a "minor abnormality", and multiple consecutive data points that exceed the set range or have abnormal fluctuations are judged as "serious abnormalities".

[0090] Next, the abnormal data is imported into the abnormal index formula, and the severity of the abnormal data is quantified through the weighted sum indicator formula to obtain the abnormal index.

[0091] The construction status is classified according to the value of the abnormality index. If the abnormality index is less than T1, the construction status is "normal"; if T1 is less than or equal to the abnormality index and less than T2, the construction status is "warning"; if the abnormality index is greater than or equal to T2, the construction status is "emergency".

[0092] According to the construction status classification results, the corresponding control instructions are generated. If the construction status is "normal", no control instructions are generated; if the construction status is "warning", the instructions of "pay attention to monitoring" or "slightly adjust parameters" are generated; if the construction status is "emergency", the instructions of "immediately stop for inspection" or "largely adjust parameters" are generated.

[0093] 4. Feedback and execution of control instructions:

[0094] The command generation and feedback module encodes and packages the generated control commands and transmits them to the IoT server through the network. The IoT server receives and parses the control commands and finds the corresponding front-end automation device according to the device ID information in the command.

[0095] The IoT server sends the control instructions to the matched front-end automation equipment. After receiving and parsing the control instructions, the front-end automation equipment adjusts the equipment parameters according to the instruction content. For example, if the instruction requires adjusting the concrete pouring speed, the front-end automation equipment will adjust the operating speed of the pumping equipment accordingly.

[0096] After executing the control instructions, the front-end automation equipment will feed back the execution results to the IoT server. The IoT server receives the execution results, stores and analyzes them, so as to further optimize and improve the construction process.

[0097] It can be seen that through the above implementation steps, this method realizes the real-time monitoring and control of the construction status of the super-high corbel column, effectively improving the construction quality and safety. At the same time, this method also has good scalability and flexibility, and can be customized and optimized according to actual needs.

[0098] The above shows and describes the basic principles and main features of the present invention and the advantages of the invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A real-time monitoring and control method for the construction status of super-high corbel columns based on the Internet of Things, characterized in that Including: S1. Obtain construction status data through the Internet of Things server, and import it into the detection algorithm after preprocessing; S2. The detection algorithm identifies abnormal data and imports it into the evaluation algorithm. The abnormal data are data points that exceed the set range or have abnormal fluctuations; S3. The evaluation algorithm evaluates the construction status in real time and generates a control instruction, which is fed back to the front-end automation equipment through the Internet of Things server for parameter adjustment.

2. According to the method of claim 1, the real-time monitoring and control method of the construction status of the super-high corbel column based on the Internet of Things is characterized by: The construction status data are uploaded in real time by various sensors deployed at the construction site of the super-high corbel column. The sensors include stress sensors, displacement sensors, temperature sensors, and humidity sensors. All types of sensors are connected to the Internet of Things server through a channel based on the TCP / IP secure transmission protocol.

3. The method for real-time monitoring and control of the construction status of a super-high corbel column based on the Internet of Things according to claim 1 is characterized in that In S1, the specific steps for preprocessing the construction status data include: S1.

1. Format conversion; S1.

2. Select the timestamp, sensor type, measured value, and unit included in the construction status data, and use regular expressions to parse the values of each field; S1.

3. Convert the extracted field values to the corresponding data types and map them to a JSON object to complete the conversion; S1.

4. Verify whether the converted data conforms to the expected format and range. If so, store it in a computer-readable storage medium; otherwise, throw an exception.

4. The method for real-time monitoring and control of the construction status of a super-high corbel column based on the Internet of Things according to claim 1 is characterized in that In S2, the specific steps of the detection algorithm include: S2.

1. Calculate the mean and standard deviation; S2.

2. Determine the normal data range according to the 3σ principle; S2.

3. Traverse the data set, check whether each data point is within the normal data range, and mark the abnormal data in the original data set.

5. The method for real-time monitoring and control of the construction status of a super-high corbel column based on the Internet of Things according to claim 1 is characterized in that In S3, the specific steps of the evaluation algorithm include: S3.

1. Conduct a preliminary classification based on the statistical characteristics of the abnormal data. If the abnormal data is a single data point that exceeds the set range, it is judged as "slightly abnormal". If multiple consecutive data points exceed the set range or have abnormal fluctuations, it is judged as "severely abnormal"; S3.

2. Import the data into the abnormal index formula, and quantify the severity of the abnormal data through the weighted summation index formula; S3.

2. Classify the construction status according to the value of the abnormal index. If the abnormal index < T1, the construction status is "normal"; if T1 ≤ abnormal index < T2, the construction status is "warning"; if the abnormal index ≥ T2, the construction status is "urgent"; S3.

3. Generate corresponding control instructions according to the classification result of the construction status. If the construction status is "normal", no control instruction is generated; if the construction status is "warning", generate instructions such as "pay attention to monitoring" or "slightly adjust parameters"; if the construction status is "urgent", generate instructions such as "immediately stop the machine for inspection" or "greatly adjust parameters".

6. The method for real-time monitoring and control of the construction status of a super-high corbel column based on the Internet of Things according to claim 1 is characterized in that In S3, the specific steps for feedback of the control instruction include: S3.

4. Represent the control instruction as C, where C contains specific device ID, parameter name to be adjusted, and target value information. Here, C is a string or data structure containing instruction content and parameter adjustment information. S3.5, encode and package the control instructions, the processing process is represented by Encoded_C=Encode(C), where Encode is an encoding function, which converts the control instructions into a format suitable for network transmission; S3.6, transmitting the encoded control instruction to the IoT server through the network. The transmission process can be expressed as Send(Encoded_C, Server_Address), where Send is a sending function and Server_Address is the address of the IoT server; S3.7, the IoT server receives and parses the control instruction, and the parsing process is represented by Decoded_C=Decode(Encoded_C), where Decode is a decoding function, which restores the received encoded instruction Encoded_C to the original control instruction C; S3.8, the IoT server parses the control instruction C, and finds the corresponding front-end automation device according to the device ID information in the instruction. The matching process is expressed as Device = Match (C, Device_List), where Match is a matching function and Device_List is a list of front-end automation devices; S3.9, the IoT server sends the control instruction to the matched front-end automation device, and the sending process is represented by SendToDevice(C, Device), where SendToDevice is a sending function, which sends the control instruction C to the matched device Device; S3.10, the front-end automation device receives and parses the control instruction, and then adjusts the device parameters according to the instruction content, but the execution process is represented as Execute(C), where Execute is an execution function, which performs corresponding operations on the device according to the parameter adjustment information in the control instruction C; S3.

11. After executing the control instruction, the front-end automation device can feed back the execution result to the IoT server. The execution process is expressed as Feedback = ExecuteResult (C), where ExecuteResult is a function that returns the execution result of the control instruction C.

7. A real-time monitoring and control system for the construction status of super-high corbel columns based on the Internet of Things, comprising a data acquisition module, a data processing module, a detection algorithm module, an evaluation algorithm module, an instruction generation and feedback module, and a front-end automation equipment interface module, characterized in that: The data acquisition module is responsible for collecting real-time construction status data from stress sensors, displacement sensors, temperature sensors, humidity sensors, etc. deployed at the super-high corbel construction site. The sensors are connected to the IoT server through the TCP / IP secure transmission protocol to form a stable data acquisition network. The sensor data is directly uploaded to the IoT server for subsequent processing; The data processing module pre-processes the construction status data, including format conversion, field parsing, data type mapping and data verification. As a data processing center, the module receives raw data and outputs formatted and verified data, receives data from the data acquisition module, and passes the results to the detection algorithm module after processing; The detection algorithm module identifies abnormal data, including data points that exceed the set range or fluctuate abnormally, implements anomaly detection based on the 3σ principle, receives data from the data processing module, marks abnormal data, and passes it to the evaluation algorithm module; The evaluation algorithm module evaluates the construction status according to the abnormal data and generates control instructions, which includes preliminary classification, abnormal index calculation, construction status classification and control instruction generation submodules, receives data from the detection algorithm module, generates control instructions after evaluation and transmits them to the instruction generation and feedback module; The instruction generation and feedback module encodes and packages the control instructions and sends them to the IoT server, and then forwards them to the front-end automation equipment, and receives feedback on the execution results at the same time, including instruction encoding, packaging, sending, parsing, matching, and execution result receiving functions, forming a closed-loop interaction with the IoT server and the front-end automation equipment to ensure accurate communication and execution of the instructions; The front-end automation equipment interface module receives the control instructions forwarded by the Internet of Things server, parses and executes them, adjusts the equipment parameters, and serves as the communication interface of the front-end automation equipment. It supports multiple communication protocols to interact with the equipment, exchanges data with the instruction generation and feedback module through the Internet of Things server, and feeds back the execution results after executing the control instructions.

8. The real-time monitoring and control system for the construction status of super-high corbel columns based on the Internet of Things according to claim 7 is characterized in that: The data acquisition module and the data processing module upload and receive data through the Internet of Things server to ensure the real-time nature of the data; after the data processing module outputs formatted data, the detection algorithm module directly reads and analyzes it; after the detection algorithm module marks abnormal data, the evaluation algorithm module uses these data to evaluate the construction status; after the evaluation algorithm module generates control instructions, the instruction generation and feedback module is responsible for encoding, packaging and sending instructions, and the instructions are forwarded to the front-end automation equipment through the Internet of Things server, and feedback on the execution results is received at the same time; the Internet of Things server acts as a transfer station to receive, parse and forward instructions, as well as return the execution results.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method described in any one of claims 1 to 6 is implemented.

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