A digital acceptance management platform for hydraulic engineering construction quality

Abstract

The application relates to a water conservancy construction quality digital acceptance management platform, which comprises a data acquisition module, a data transmission module, a data processing and storage module, an acceptance management module, a quality evaluation module, an early warning module and a user interaction module; the data acquisition module is configured to acquire various construction quality data in a water conservancy construction process according to a preset acquisition frequency; the acceptance management module is configured to develop a process and a template according to a water conservancy acceptance specification, optimize acceptance task allocation by using an acceptance process optimization model, and calculate acceptance scores by using an acceptance score calculation model; the quality evaluation module is configured to construct an evaluation model, and perform deep analysis and evaluation on the acquired construction quality data by using the evaluation model and generate a quality evaluation report; the water conservancy construction quality digital acceptance management platform can improve acceptance efficiency, can also reduce the interference of human factors on acceptance results, and can make the acceptance results more objective and accurate.

Description

Technical Field

[0001] This application relates to the field of water conservancy project management technology, specifically to a digital acceptance management platform for water conservancy project construction quality. Background Technology

[0002] As a crucial component of national economic infrastructure, the construction quality of water conservancy projects directly impacts the safety of people's lives and property and the sustainable development of society and the economy. Traditional water conservancy project construction quality acceptance processes rely primarily on manual recording, paper document transmission, and on-site inspections, which have numerous drawbacks. For example, manual recording is prone to data errors and omissions; paper documents are easily damaged or lost during transmission and storage, and are extremely inconvenient for retrieval and statistical analysis; on-site inspections are heavily influenced by human factors, and acceptance standards are difficult to standardize, resulting in a lack of objectivity and accuracy in the acceptance results. Furthermore, traditional acceptance methods cannot achieve real-time monitoring of the construction process and dynamic data analysis, making it difficult to promptly identify and resolve construction quality problems, seriously affecting the construction progress and quality of water conservancy projects. With the rapid development of information technology, applying digital technology to the management of water conservancy project construction quality acceptance has become an inevitable trend; however, currently, there is a lack of a comprehensive digital platform suitable for the management of water conservancy project construction quality acceptance in the market. Summary of the Invention

[0003] This application provides a digital acceptance management platform for the construction quality of water conservancy projects. This platform can improve acceptance efficiency and reduce the interference of human factors on the acceptance results, making the acceptance results more objective and accurate.

[0004] The digital acceptance management platform for water conservancy project construction quality provided in this application includes a data acquisition module, a data transmission module, a data processing and storage module, an acceptance management module, a quality assessment module, an early warning module, and a user interaction module. The data acquisition module is configured to collect various types of construction quality data during the water conservancy project construction process according to a preset acquisition frequency. The acceptance management module is configured to formulate processes and templates based on water conservancy project acceptance specifications, optimize the allocation of acceptance tasks using an acceptance process optimization model, and calculate acceptance scores using an acceptance scoring calculation model. The quality assessment module is configured to construct an assessment model, and use the assessment model to perform in-depth analysis and evaluation of the collected construction quality data and generate a quality assessment report. The evaluation model in the quality assessment module adopts the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The AHP is used to construct a judgment matrix, calculate the maximum eigenvalue, and perform a consistency check to determine the weights of each evaluation factor. The AHP employs the following strategies: Construct a judgment matrix The matrix elements were determined by experts scoring the relative importance of each factor. Calculate the largest eigenvalue using the power method. ; Perform a consistency check and calculate. Then calculate the random consistency ratio. ,in, Obtained by looking up a table, when At that time, the judgment matrix has satisfactory consistency; in, For the judgment matrix; To determine the importance of a matrix element relative to factor j; For the number of factors; As a consistency indicator, As a random consistency indicator, The random consistency ratio; The fuzzy comprehensive evaluation method employs the following strategies: Determine the set of factors That is, the set of all factors that affect quality; Determine the comment set ; Constructing a fuzzy relation matrix The matrix elements are determined through expert scoring or statistical methods to represent factors. Comments Membership degree; Calculate the comprehensive evaluation results , This is the weight vector.

[0005] In one alternative, the data acquisition module includes various types of sensors and acquisition devices, including an ultrasonic sensor for measuring concrete strength, a compaction sensor for monitoring earthwork compaction, and a water level and flow meter for detecting water level and flow rate. The collected construction quality data includes physical quality data of the project, construction process data, and raw material quality data, and the data collection frequency formula is as follows: In the formula, For the sampling frequency, The characteristic time constant of the construction process, The safety margin factor set for the system; During data acquisition, after determining the acquisition frequency, the sources of error are analyzed and corrected using a data acquisition error model. The formula for the data acquisition error model is as follows: In the formula, The total measurement error is... This is due to the inherent error of the system. Here, λ is the environmental impact coefficient, and λ is the time decay coefficient. This is random noise error. This refers to the transmission time.

[0006] In one optional embodiment, the data transmission module is configured to encrypt and transmit the collected data; the input terminal of the data transmission module is electrically connected to the output terminal of the data acquisition module; the data transmission module employs a transmission method combining wireless and wired networks, and uses the AES encryption algorithm to encrypt the collected data. The data transmission reliability formula for the AES encryption algorithm is: In the formula, For transmission reliability, the value range is: , Here, d is the distance attenuation coefficient, and d is the transmission distance. For frequency interference coefficient, For transmission frequency; The data encryption security assessment model in the AES encryption algorithm is as follows: In the formula, For encryption security, The encryption key length is used to determine the encryption key length. Assess encryption security.

[0007] In one alternative scheme, the data processing and storage module is configured to receive the collected data transmitted by the data transmission module, clean, classify and integrate the received data, store the processed data in a distributed database, and establish an indexing and backup mechanism. The data cleaning formula used by the data processing and storage module is as follows: Weights are calculated using kernel functions. The received data is cleaned to remove noise and duplicate data; where, For the cleaned data points, For data points within the neighborhood, For data points The neighborhood, The weight is calculated using the following formula: In the formula, The distance between data points This refers to the kernel function bandwidth parameter.

[0008] In one alternative approach, the data processing and storage module employs a distributed storage system performance model during data storage, as shown in the formula: In the formula, For the total response time, For read latency, For write delay time, Number of data shards For network bandwidth, This represents the number of storage nodes.

[0009] In one alternative approach, the formula for the acceptance process optimization model in the acceptance management module is: The constraints are: ; , In the formula, The cost of assigning task i to person j. , which is the decision variable, representing the task allocation situation.

[0010] In one optional scheme, the acceptance scoring calculation model in the acceptance management module calculates the score using the following formula: In the formula, Let i be the weight of the i-th indicator. Let i be the score for the i-th indicator, and S represents the total score.

[0011] In one optional embodiment, the early warning module is configured to monitor construction quality data in real time. When the construction quality data exceeds a preset threshold range and / or shows an abnormal trend, an early warning mechanism is triggered. The early warning methods of the early warning module include sound alarms, SMS reminders, and system pop-up prompts.

[0012] In one optional scheme, the early warning module employs an early warning threshold determination method, the formula of which is: The specific strategy is as follows: collect historical data within a preset time period and calculate the average of the historical data. and historical data standard deviation Based on the project's risk tolerance and experience, a threshold coefficient is set. ,Will , , Substitution Calculate the warning thresholds respectively and lower warning threshold ; An anomaly trend detection algorithm calculates a trend indicator, compares it with a set trend threshold T, and determines whether the data exhibits an anomaly trend. The anomaly trend detection algorithm employs the following strategies: The formula for the anomaly trend detection algorithm is: ,in, For the i-th data, For the number of data; if If an abnormal trend is detected, an early warning mechanism will be triggered.

[0013] In one alternative, the user interaction module can provide a multi-terminal operation interface and support multi-terminal access, including computer, mobile phone and tablet, and users can perform data query, report generation and acceptance application submission through the user interaction module. The formula for the interface interaction efficiency model in the user interaction module is: In the formula, For better interaction efficiency, The number of clicks required to complete the task. The time required to complete the task, For cognitive load, and The calculation formula is: , Let i be the amount of information for the i-th interface element. Let be the attention weight of the i-th element.

[0014] The beneficial effects of this application are as follows: The digital acceptance management platform for water conservancy project construction quality in this application automates and digitizes the acceptance process through digital technology, reducing the time spent on manual operation and paper document processing, and greatly improving acceptance efficiency. At the same time, the system can automatically retrieve relevant data and materials, providing convenient reference for acceptance personnel and further accelerating the acceptance process. In addition, the quality assessment module uses scientific assessment models and algorithms to reduce the interference of human factors on the acceptance results, making the acceptance results more objective and accurate.

[0015] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0016] Figure 1 A schematic diagram of the system flow for a digital acceptance management platform for the construction quality of water conservancy projects.

[0017] Attached reference numerals: Data acquisition module 1, data transmission module 2, data processing and storage module 3, acceptance management module 4, quality assessment module 5, early warning module 6, user interaction module 7.

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0020] It should be understood that the described embodiments are merely some embodiments of this application, and not all embodiments. All other technical solutions obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0023] like Figure 1 As shown in the figure, this application provides a digital acceptance management platform for water conservancy project construction quality. This platform mainly includes a data acquisition module 1, a data transmission module 2, a data processing and storage module 3, an acceptance management module 4, a quality assessment module 5, an early warning module 6, and a user interaction module 7. The data acquisition module 1 is deployed at the construction site and includes various types of sensors and acquisition devices. The data acquisition module 1 is configured to collect various construction quality data during the water conservancy project construction process according to a preset acquisition frequency. The data acquisition module 1 includes various types of sensors and acquisition devices, including an ultrasonic sensor for measuring concrete strength, a compaction sensor for monitoring earthwork compaction, and a water level and flow meter for detecting water level and flow rate.

[0024] The construction quality data collected by data acquisition module 1 includes, but is not limited to, engineering entity quality data, construction process data, and raw material quality data. The data acquisition frequency formula during the data acquisition process is as follows: In the formula, For the sampling frequency, The characteristic time constant of the construction process, The system is configured with a safety margin factor. During data acquisition, after determining the acquisition frequency, the sources of error are analyzed and corrected using a data acquisition error model. The formula for the data acquisition error model is as follows: In the formula, The total measurement error is... This is due to the inherent error of the system. Here, λ is the environmental impact coefficient, and λ is the time decay coefficient. This is random noise error. This refers to the transmission time.

[0025] Data transmission module 2 is configured to encrypt and transmit the collected data; the input of data transmission module 2 is electrically connected to the output of data acquisition module 1; data transmission module 2 adopts a transmission method combining wireless and wired networks, and uses the AES encryption algorithm to encrypt the collected data. The data transmission reliability formula of the AES encryption algorithm is: In the formula, For transmission reliability, the value range is: , Here, d is the distance attenuation coefficient, and d is the transmission distance. For frequency interference coefficient, For transmission frequency; The data encryption security assessment model in the AES encryption algorithm is as follows: In the formula, For encryption security, The encryption key length is used to determine the encryption key length. Assess encryption security.

[0026] The data processing and storage module 3 is configured to receive the collected data transmitted by the data transmission module 2, clean, classify, and integrate the received data, store the processed data in a distributed database, and establish an indexing and backup mechanism. The formula for data cleaning by the data processing and storage module 3 is as follows: Weights are calculated using kernel functions. The received data is cleaned to remove noise and duplicate data; where, For the cleaned data points, For data points within the neighborhood, For data points The neighborhood, The weight is calculated using the following formula: In the formula, The distance between data points This refers to the kernel function bandwidth parameter.

[0027] Furthermore, data processing and storage module 3 adopts a distributed storage system performance model during data storage, with the following formula: In the formula, For the total response time, For read latency, For write delay time, Number of data shards For network bandwidth, This represents the number of storage nodes.

[0028] The Acceptance Management Module 4 is configured to develop processes and templates based on the water conservancy project acceptance specifications, optimize the allocation of acceptance tasks using the acceptance process optimization model, and calculate acceptance scores using the acceptance scoring calculation model.

[0029] The formula for the acceptance process optimization model in Module 4 of Acceptance Management is: The constraints are: ; , In the formula, The cost of assigning task i to person j. Let be the decision variable, representing the task allocation. The acceptance scoring calculation model in Acceptance Management Module 4 calculates the score using the following formula: In the formula, Let i be the weight of the i-th indicator. Let i be the score for the i-th indicator, and S represents the total score.

[0030] Quality Assessment Module 5 is configured to build an assessment model, and then use this model to perform in-depth analysis and evaluation of the collected construction quality data, generating a quality assessment report. The assessment model in Module 5 employs the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation. The AHP is used to construct a judgment matrix, calculate the maximum eigenvalue, and perform consistency checks to determine the weights of each assessment factor. The AHP executes the following strategies: Construct a judgment matrix The matrix elements were determined by experts scoring the relative importance of each factor. Calculate the largest eigenvalue using the power method. ; Perform a consistency check and calculate. Then calculate the random consistency ratio. ,in, Obtained by looking up a table, when At that time, the judgment matrix has satisfactory consistency; in, For the judgment matrix; To determine the importance of a matrix element relative to factor j; For the number of factors; As a consistency indicator, As a random consistency indicator, The random consistency ratio; The fuzzy comprehensive evaluation method employs the following strategies: Determine the set of factors That is, the set of all factors that affect quality; Determine the comment set ; Constructing a fuzzy relation matrix The matrix elements are determined through expert scoring or statistical methods to represent factors. Comments Membership degree; Calculate the comprehensive evaluation results , This is the weight vector.

[0031] The early warning module 6 is configured to monitor construction quality data in real time. When construction quality data exceeds a preset threshold range and / or exhibits an abnormal trend, an early warning mechanism is triggered. The early warning module 6 provides warnings via sound alarms, SMS alerts, and system pop-up notifications. The early warning module 6 uses a method for determining the early warning threshold, with the following formula: The specific strategy is as follows: collect historical data within a preset time period and calculate the average of the historical data. and historical data standard deviation Based on the project's risk tolerance and experience, a threshold coefficient is set. ,Will , , Substitution Calculate the warning thresholds respectively and lower warning threshold ; An anomaly trend detection algorithm calculates a trend indicator, compares it with a set trend threshold T, and determines whether the data exhibits an anomaly trend. The anomaly trend detection algorithm employs the following strategies: The formula for the anomaly trend detection algorithm is: ,in, For the i-th data, For the number of data; if If an abnormal trend is detected, an early warning mechanism will be triggered.

[0032] User interaction module 7 can provide a multi-terminal operation interface, optimize the interface based on the interface interaction efficiency model, and support multi-terminal access, including computer, mobile phone and tablet. Users can perform data query, report generation and acceptance application submission through user interaction module 7.

[0033] The formula for the interface interaction efficiency model in User Interaction Module 7 is: In the formula, For better interaction efficiency, The number of clicks required to complete the task. The time required to complete the task, For cognitive load, and The calculation formula is: , Let i be the amount of information for the i-th interface element. Let be the attention weight of the i-th element.

[0034] This digital acceptance management platform for water conservancy project construction quality automates and digitizes the acceptance process through digital technology, reducing the time spent on manual operations and paper document processing, and greatly improving acceptance efficiency. At the same time, the system can automatically retrieve relevant data and materials, providing convenient references for acceptance personnel and further accelerating the acceptance process. In addition, the quality assessment module 5 uses scientific assessment models and algorithms to reduce the interference of human factors on the acceptance results, making the acceptance results more objective and accurate.

[0035] Compared with existing technologies, this digital acceptance management platform for water conservancy project construction quality enables full-process monitoring of water conservancy project construction quality, from the collection, transmission, processing, analysis and evaluation of construction data. Through real-time monitoring and early warning functions, it can promptly detect and resolve quality problems in the construction process and effectively prevent the occurrence of quality accidents.

[0036] Taking a large reservoir dam reinforcement project as an example, the digital acceptance management platform for the construction quality of this water conservancy project is deployed at the construction site and project management office. Data acquisition module 1 is deployed at key construction sites such as dam pouring, earthwork backfilling, and water pipeline installation. Data transmission module 2 uses a combination of 4G wireless network and wired fiber optic network to achieve data transmission. The system runs on a high-performance server cluster and is equipped with multiple computers, mobile phones, and tablets for different personnel to use.

[0037] Data acquisition module 1: Ultrasonic sensors were deployed in the dam's concrete pouring area to measure concrete strength. The sensors automatically collected data every 10 cubic meters of concrete poured. This data collection frequency was determined according to a formula... Analysis of the characteristic time constant of the pouring process Approximately 30 minutes, set a safety margin factor. The value is 1.2, and the sampling frequency is calculated. times per hour. Simultaneously, a data acquisition error model is utilized. Analysis of errors, including inherent system errors The environmental influence coefficient was determined to be ±0.5 MPa after sensor calibration. The experimentally determined value was 0.3, the time decay coefficient λ was 0.01, and the random noise error δ was ±0.2MPa according to statistical analysis. In the earthwork backfilling area, the compaction sensor collected the earthwork compaction data in real time, and the portable terminal was used by on-site construction personnel to record data such as the attendance of construction personnel and the operating parameters of construction equipment.

[0038] Data transmission module 2: The collected data is transmitted to the data processing center via 4G wireless network and wired fiber optic network. During data transmission, the AES encryption algorithm is used to encrypt the data, with the encryption key length n set to 256 bits, based on a data encryption security assessment model. The encryption security was calculated. Following point 1, ensure data transmission security. Simultaneously, based on the data transmission reliability formula... Optimized transmission was implemented, with a distance attenuation coefficient α of 0.01, an average transmission distance d of 5 kilometers, a frequency interference coefficient β of 0.005, and a transmission frequency of... For 2000MHz, time decay coefficient The value is 0.001, and the average transmission time t is 2 seconds. Calculations show that... This ensures reliable data transmission.

[0039] Data processing and storage module 3: The received data first enters the data processing and storage module 3. Data cleaning algorithms are then used. In this context, the kernel function bandwidth parameter σ is set to 1, and the distance between data points is... Euclidean distance calculations were used to clean the concrete strength data, removing abnormal data caused by transient sensor malfunctions. A distributed storage architecture was employed, dividing the data into 100 fragments (N=100), with a network bandwidth B of 100Mbps, 10 storage nodes M, and a read latency of [missing information]. The write latency is 10ms. The response time is 15ms, based on the performance model of distributed storage systems. The total response time was calculated. It meets the needs for fast data storage and retrieval, and establishes a daily automatic backup mechanism.

[0040] Acceptance Management Module 4: During the project acceptance phase, acceptance procedures and templates were developed based on the water conservancy project acceptance specifications, including dam foundation treatment, concrete pouring, and metal structure installation. An acceptance process optimization model was then employed. The constraints are: ; , ,in, The cost of assigning task i to person j. Let be the decision variable, representing the task allocation. The concrete pouring acceptance task is assigned to three quality inspection personnel with relevant experience to minimize the acceptance cost.

[0041] In the acceptance of concrete pouring, an acceptance scoring calculation model is used. , Let i be the weight of the i-th indicator. Let i be the score for the i-th indicator, and S represents the total score. The weights for concrete strength (0.4), pouring appearance quality (0.3), and pouring dimensional deviation (0.3) are determined. The scores for each indicator are as follows: point, point, The acceptance score was calculated as S = 0.4 × 90 + 0.3 × 85 + 0.3 × 88 = 88.1 points, achieving online acceptance and paperless management.

[0042] Quality Assessment Module 5: The analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method were used to assess the construction quality of the dam. In the AHP, a judgment matrix was constructed. For example, for three factors—concrete strength, reinforcement quality, and construction technology—expert scores determined the elements of the judgment matrix, and the maximum eigenvalue was calculated. After a consistency test, the random consistency ratio CR < 0.1, and the weights of each factor were determined. In the fuzzy comprehensive evaluation, the factor set U = {concrete strength, steel reinforcement quality, construction technology, appearance quality} and the evaluation set V = {excellent, good, average, poor} were determined. A fuzzy relation matrix R was constructed through expert scoring, and combined with the weight vector W, the comprehensive evaluation result was calculated. The overall quality assessment of the dam construction was rated as "good," and a quality assessment report was generated, proposing improvement suggestions such as insufficient thickness of the steel reinforcement protective layer in some areas.

[0043] Early warning module 6: For dam settlement monitoring data, the method of determining early warning thresholds is used. A threshold is set, and the average value is calculated based on the historical settlement data of 3 months. 5mm, standard deviation 1mm, threshold coefficient Taking 2, we obtain the upper warning threshold. mm, lower warning threshold mm. When the settlement at a certain monitoring point reaches 7.5 mm, according to the abnormal trend detection algorithm... If the system detects abnormal data trends, it will immediately alert the project manager via sound alarm, SMS, and system pop-up notification, while also recording the warning information for subsequent traceability and analysis.

[0044] User interaction module 7: Project managers can log in to the platform via computer, mobile phone, and tablet. (Based on a user interface efficiency model.) ,in: The interface layout has been optimized, such as placing frequently used data query and acceptance application functions in a prominent position on the homepage to reduce operation steps. In a single concrete pouring data query operation, the number of clicks to complete the task is [number missing]. It takes 3 times and takes a total of 10 minutes. The calculation shows a high interface interaction efficiency of 10 seconds. Simultaneously, construction workers, technicians, and managers can use the platform's online communication function to exchange information on construction quality issues in real time and collaboratively resolve problems.

[0045] As can be seen from the above embodiments, the digital acceptance management platform for water conservancy project construction quality of the present invention can effectively realize the digital and intelligent management of water conservancy project construction quality, improve acceptance efficiency and quality, and ensure the smooth progress of water conservancy project construction.

[0046] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A digital acceptance management platform for the construction quality of water conservancy projects, characterized in that, The system includes a data acquisition module, a data transmission module, a data processing and storage module, an acceptance management module, a quality assessment module, an early warning module, and a user interaction module. The data acquisition module is configured to collect various construction quality data during the water conservancy project construction process according to a preset acquisition frequency. The acceptance management module is configured to develop processes and templates based on water conservancy project acceptance standards, optimize acceptance task allocation using an acceptance process optimization model, and calculate acceptance scores using an acceptance scoring calculation model. The quality assessment module is configured to construct an assessment model, perform in-depth analysis and evaluation of the collected construction quality data using the assessment model, and generate a quality assessment report. The evaluation model in the quality assessment module adopts the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The AHP is used to construct a judgment matrix, calculate the maximum eigenvalue, and perform a consistency check to determine the weights of each evaluation factor. The AHP employs the following strategies: Construct a judgment matrix The matrix elements were determined by experts scoring the relative importance of each factor. Calculate the largest eigenvalue using the power method. ; Perform a consistency check and calculate. Then calculate the random consistency ratio. ,in, Obtained by looking up a table, when At that time, the judgment matrix has satisfactory consistency; in, For the judgment matrix; To determine the importance of a matrix element relative to factor j; For the number of factors; As a consistency indicator, As a random consistency indicator, The random consistency ratio; The fuzzy comprehensive evaluation method employs the following strategies: Determine the set of factors That is, the set of all factors that affect quality; Determine the comment set ; Constructing a fuzzy relation matrix The matrix elements are determined through expert scoring or statistical methods to represent factors. Comments Membership degree; Calculate the comprehensive evaluation results , This is the weight vector.

2. The digital acceptance management platform for water conservancy project construction quality according to claim 1, characterized in that, The data acquisition module includes various types of sensors and acquisition devices, including ultrasonic sensors for measuring concrete strength, compaction sensors for monitoring soil compaction, and water level and flow meters for detecting water level and flow rate. The collected construction quality data includes physical quality data of the project, construction process data, and raw material quality data, and the data collection frequency formula is as follows: In the formula, For the sampling frequency, The characteristic time constant of the construction process, The safety margin factor set for the system; During data acquisition, after determining the acquisition frequency, the sources of error are analyzed and corrected using a data acquisition error model. The formula for the data acquisition error model is as follows: In the formula, The total measurement error is... This is due to the inherent error of the system. Here, λ is the environmental impact coefficient, and λ is the time decay coefficient. This is random noise error. This refers to the transmission time.

3. The digital acceptance management platform for water conservancy project construction quality according to claim 2, characterized in that, The data transmission module is configured to encrypt and transmit the collected data; the input terminal of the data transmission module is electrically connected to the output terminal of the data acquisition module; the data transmission module adopts a transmission method combining wireless and wired networks, and uses the AES encryption algorithm to encrypt the collected data. The data transmission reliability formula of the AES encryption algorithm is: In the formula, For transmission reliability, the value range is: , Here, d is the distance attenuation coefficient, and d is the transmission distance. For frequency interference coefficient, For transmission frequency; The data encryption security assessment model in the AES encryption algorithm is as follows: In the formula, For encryption security, The encryption key length is used to determine the encryption key length. Assess encryption security.

4. The digital acceptance management platform for water conservancy project construction quality according to any one of claims 1-3, characterized in that, The data processing and storage module is configured to receive the collected data transmitted by the data transmission module, clean, classify and integrate the received data, store the processed data in a distributed database, and establish an indexing and backup mechanism. The data cleaning formula used by the data processing and storage module is as follows: Weights are calculated using kernel functions. The received data is cleaned to remove noise and duplicate data; where, For the cleaned data points, For data points within the neighborhood, For data points The neighborhood, The weight is calculated using the following formula: In the formula, The distance between data points This refers to the kernel function bandwidth parameter.

5. The digital acceptance management platform for water conservancy project construction quality according to claim 4, characterized in that, The data processing and storage module adopts a distributed storage system performance model during data storage, as shown in the formula: In the formula, For the total response time, For read latency, This is the write delay time. Number of data shards For network bandwidth, This represents the number of storage nodes.

6. The digital acceptance management platform for water conservancy project construction quality according to any one of claims 1-3 or 5, characterized in that, The formula for the acceptance process optimization model in the acceptance management module is: The constraints are: ; , In the formula, The cost of assigning task i to person j. , which is the decision variable, representing the task allocation situation.

7. The digital acceptance management platform for water conservancy project construction quality according to claim 6, characterized in that, The acceptance scoring model in the acceptance management module calculates the score using the following formula: In the formula, Let i be the weight of the i-th indicator. Let i be the score for the i-th indicator, and S represents the total score.

8. The digital acceptance management platform for water conservancy project construction quality according to any one of claims 1-3, 5, or 7, characterized in that, The early warning module is configured to monitor construction quality data in real time. When the construction quality data exceeds the preset threshold range and / or shows an abnormal trend, an early warning mechanism is triggered. The early warning methods of the early warning module include sound alarm, SMS reminder, and system pop-up prompts.

9. The digital acceptance management platform for water conservancy project construction quality according to claim 8, characterized in that, The early warning module uses an early warning threshold determination method, the formula of which is: The specific strategy is as follows: collect historical data within a preset time period and calculate the average of the historical data. and historical data standard deviation Based on the project's risk tolerance and experience, a threshold coefficient is set. ,Will , , Substitution Calculate the warning thresholds respectively and lower warning threshold ; An anomaly trend detection algorithm calculates a trend indicator, compares it with a set trend threshold T, and determines whether the data exhibits an anomaly trend. The anomaly trend detection algorithm employs the following strategies: The formula for the anomaly trend detection algorithm is: ,in, For the i-th data, For the number of data; if If an abnormal trend is detected, an early warning mechanism will be triggered.

10. The digital acceptance management platform for water conservancy project construction quality according to any one of claims 1-3, 5, 7, or 9, characterized in that, The user interaction module can provide a multi-terminal operation interface and support multi-terminal access, including computer, mobile phone and tablet. Users can perform data query, report generation and acceptance application submission through the user interaction module. The formula for the interface interaction efficiency model in the user interaction module is: In the formula, For better interaction efficiency, The number of clicks required to complete the task. The time required to complete the task, For cognitive load, and The calculation formula is: , Let i be the amount of information for the i-th interface element. Let be the attention weight of the i-th element.

Images