Anti-corrosion coating distribution optimization management platform and method based on Internet of Things

Abstract

The invention discloses an anti-corrosion coating distribution optimization management platform and method based on the Internet of Things. The method comprises the following steps: collecting stock information, usage amount, residual amount and usage state data of paint in real time through Internet of Things equipment, and transmitting the data to a cloud data processing platform; through an intelligent algorithm and real-time data synchronization, the platform can accurately predict the coating demand and dynamically adjust the release amount, excess or shortage is avoided, waste is reduced, and the construction efficiency is improved. And real-time synchronization ensures that managers can timely master inventory and consumption conditions and quickly respond to field changes. The paint loss coefficient is introduced, the platform can accurately adjust the release amount according to the construction environment and operation, the paint utilization is maximized, the resource allocation is optimized, the cost is reduced, the waste is reduced, and the environmental burden is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-corrosion coating issuance optimization, and in particular to an anti-corrosion coating issuance optimization management platform and method based on the Internet of Things. Background Art

[0002] The optimization management of anti-corrosion coating issuance based on the Internet of Things refers to a management system that uses the Internet of Things technology to digitize and intelligentize the entire process of coating issuance, use and management, so as to improve the accuracy of coating use, reduce waste, optimize resource allocation and ensure construction quality. Specifically, this management method installs intelligent sensors and data acquisition equipment to monitor the remaining amount, usage and distribution of coatings in real time during the use of coatings, and uploads these data to the cloud platform for analysis and processing. The system can automatically calculate and adjust the amount of coating issued based on real-time data to ensure that each batch of coatings can be accurately used for the corresponding coating needs, avoiding errors and waste caused by manual operation.

[0003] This optimized management method not only digitally tracks each step of the painting process, but also accurately calculates the painting area of ​​each steel structure unit through the combination of IoT technology and paint calculation methods, and reasonably distributes paint according to different surface types (such as inner surface, outer surface, etc.). The paint loss coefficient can also be adjusted in real time through the IoT, and it can be supplemented or adjusted according to the actual use of the paint, so as to achieve the optimal management of paint distribution, reduce paint waste and reduce overall costs. In addition, the system can provide real-time feedback on the remaining paint data, helping managers make timely supplementary decisions and improve the efficiency and accuracy of the entire construction process.

[0004] The prior art has the following deficiencies:

[0005] In the process of optimizing the management of anti-corrosion coating issuance based on the Internet of Things, the real-time and accuracy of coating usage data may be limited, leading to serious problems in the management system. Although the Internet of Things system can collect data such as coating inventory and usage in real time through sensors and smart devices, if the system has problems such as unstable network connection, sensor failure or data synchronization delay, it may lead to inaccurate inventory information or usage data. This situation will cause inaccurate coating issuance and even over-issuance or under-inventory. If the coating inventory is excessive, it may lead to coating waste and additional storage costs; if the inventory is insufficient, it may affect the construction progress, cause project delays or require urgent coating replenishment, and increase additional costs. At the same time, inaccurate coating use may also affect the quality of coating, which in turn poses a hidden danger to the anti-corrosion effect and affects the long-term safety of equipment and structures. Therefore, ensuring the real-time collection and accurate synchronization of coating usage data is crucial to ensure the rationality of coating issuance and the smooth progress of the project.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide an anti-corrosion coating distribution optimization management platform and method based on the Internet of Things. Through the combination of intelligent algorithms and real-time data synchronization mechanisms, the platform can accurately predict coating demand and dynamically adjust the distribution volume, thereby avoiding excessive or shortage of coatings, reducing waste and improving construction efficiency. Real-time synchronization ensures that managers obtain the latest inventory and consumption information, respond to on-site changes in a timely manner, and avoid the lag problem of traditional manual management. In addition, by introducing the calculation of the coating loss coefficient, the platform can accurately adjust the distribution volume according to the construction environment and operation, maximize coating utilization, reduce costs, optimize resource allocation, and reduce coating waste and environmental burden to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: an optimization management method for the issuance of anti-corrosion coatings based on the Internet of Things, comprising the following steps:

[0009] Collect paint inventory information, usage, remaining quantity and usage status data in real time through IoT devices, and transmit this data to the cloud data processing platform;

[0010] Receive and store coating usage data in the cloud data processing platform to ensure real-time synchronization of data, and process and verify the data to ensure its accuracy and completeness;

[0011] Intelligently calculate the required paint distribution volume based on paint inventory information, usage status, construction requirements and historical data, and generate an optimized distribution plan to ensure that the paint distribution volume matches actual demand;

[0012] Monitor the sensor data, network connection and system status of IoT devices in real time to detect data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism will be triggered and the management personnel will be notified to repair them.

[0013] According to the actual paint usage, the loss coefficient is calculated, and the paint issuance amount is dynamically adjusted according to the loss coefficient to optimize the use of resources and reduce paint waste.

[0014] Preferably, the inventory information, usage, remaining amount and usage status data of the coating are collected in real time through the Internet of Things devices, and the data are transmitted to the cloud data processing platform. The specific steps are as follows:

[0015] IoT devices collect real-time information on paint inventory, usage, remaining quantity, and paint usage status through monitoring equipment;

[0016] The collected paint usage data is transmitted to the cloud data processing platform via the network;

[0017] After the data is transmitted to the cloud, the data processing platform receives the paint usage information and stores it;

[0018] After receiving the paint usage data, the cloud platform will analyze and process the data in real time.

[0019] Preferably, the specific steps of receiving and storing the coating usage data in the cloud data processing platform, ensuring the real-time synchronization of the data, and processing and verifying the data to ensure the accuracy and completeness of the data are as follows:

[0020] After receiving the paint usage data on the cloud data processing platform, it will first conduct preliminary data verification;

[0021] The verified data will be stored in the database of the cloud platform;

[0022] While storing data, the cloud platform will ensure real-time synchronization of data;

[0023] After the data is stored and synchronized, the cloud platform will further process and analyze the paint usage data.

[0024] Preferably, the required paint distribution volume is intelligently calculated based on paint inventory information, usage status, construction requirements and historical data, and an optimized distribution plan is generated to ensure that the paint distribution volume matches the actual demand. The specific steps are as follows:

[0025] Based on the processed data, the cloud platform will use intelligent algorithms to calculate the required amount of paint to be distributed;

[0026] Based on the intelligent calculation of the release volume, an optimized paint release plan will be generated;

[0027] After the optimized distribution plan is generated, the cloud platform will dynamically adjust the distribution plan based on the real-time paint consumption and construction progress.

[0028] Preferably, the sensor data, network connection and system status of the IoT device are monitored in real time to detect whether there is data delay, network failure and sensor abnormality. If there is an abnormality, the alarm mechanism is triggered and the management personnel are notified to repair it. The specific steps are as follows:

[0029] In the IoT system, the sensor data of coating-related equipment must first be monitored in real time. By setting the data collection frequency and time window, ensure that the sensor data can be collected and uploaded to the cloud platform on time. During the monitoring process, the following formula is used to calculate the degree of data abnormality:

[0030] ,

[0031] In the formula, A(t) represents the abnormality at time point t, D(t) is the sensor data collected at the current time point, is the predicted value based on historical data, σ D is the standard deviation of the historical data;

[0032] In addition to monitoring sensor data, the network connection status between the device and the cloud platform is also monitored in real time. To this end, the platform will regularly check the connection status of each node to determine the operation of the network. To quantify network performance, the following formula is used to calculate the network stability index;

[0033] ,

[0034] Where S(t) is the network stability index, Δt i is the delay of the i-th data transmission, n is the number of samples;

[0035] Sensor fault detection is also crucial. The accuracy and stability of sensors directly affect the quality of data. Sensor anomaly detection uses a model based on historical behavior to calculate the normality index of the sensor status through the following formula:

[0036]

[0037] Where N(t) represents the sensor state normality index at time point t, D(t) is the current sensor reading, μ is the mean of historical data, and α is the adjustment factor;

[0038] When data delays, network failures, and sensor anomalies are detected, the alarm mechanism is triggered immediately and the relevant management personnel are notified to handle them. To improve the accuracy of the alarm, the priority of the alarm is weighted using the following formula:

[0039] P(t)=w1·A(t)+w2·(1-S(t))+w3·(1-N(t)),

[0040] Where P(t) is the priority of the alarm, A(t) is the sensor data anomaly, S(t) is the network stability index, N(t) is the sensor state normality index, w1, w2, w3 are the weight coefficients of the sensor data anomaly A(t), the network stability index S(t) and the sensor state normality index N(t), respectively.

[0041] Preferably, the loss coefficient is calculated according to the actual coating usage, and the coating distribution amount is dynamically adjusted according to the loss coefficient to optimize the use of resources and reduce the waste of coating. The specific steps are as follows:

[0042] Before calculating the loss coefficient of the paint, first collect the actual paint usage data, and obtain accurate paint consumption data by real-time tracking and recording the paint usage, and calculate the initial loss coefficient. The calculation expression is as follows;

[0043] ,

[0044] In the formula, K loss is the initial loss coefficient, S used is the amount of paint actually used, S expected The expected amount of paint calculated for the system;

[0045] After calculating the preliminary loss coefficient, the next step is to analyze and identify the various external factors that affect the loss coefficient. In order to accurately adjust the loss coefficient, the external factors are taken into account and the impact of these factors on the coating consumption is calculated. By establishing a multi-factor regression model, the influencing factors are quantified and the loss coefficient is further corrected. The form of the model is as follows:

[0046] ,

[0047] Where, X j is the jth influencing factor, β j is the coefficient of each influencing factor, is the adjusted loss coefficient, m is the total number of influencing factors;

[0048] According to the adjusted loss factor The next step is to dynamically adjust the amount of paint to optimize the efficiency of paint use. The adjusted amount is calculated using the following formula:

[0049] ,

[0050] In the formula, S adjusted is the adjusted issuance amount, S expected is the expected amount of paint used;

[0051] When the adjusted paint distribution amount is determined, the paint usage at the construction site is monitored in real time to continue to optimize and adjust the distribution amount. If the paint consumption changes significantly during the construction process, feedback data will be collected in real time and the loss coefficient will be recalculated. According to the latest loss coefficient, the future paint distribution plan will be adjusted. This process is achieved through the following dynamic optimization formula:

[0052] ,

[0053] In the formula, is the loss coefficient after dynamic adjustment, and Y is the adjustment factor.

[0054] The anti-corrosion coating issuance optimization management platform based on the Internet of Things includes a coating data acquisition module, a data reception and synchronization module, a coating issuance calculation and optimization module, a real-time monitoring and anomaly detection module, and a loss coefficient calculation and adjustment module:

[0055] The paint data collection module collects paint inventory information, usage, remaining quantity and usage status data in real time through IoT devices, and transmits this data to the cloud data processing platform;

[0056] Data receiving and synchronization module receives and stores paint usage data in the cloud data processing platform to ensure real-time synchronization of data, and processes and verifies the data to ensure its accuracy and completeness;

[0057] The paint distribution calculation and optimization module intelligently calculates the required paint distribution volume based on paint inventory information, usage status, construction requirements and historical data, and generates an optimized distribution plan to ensure that the paint distribution volume matches the actual demand;

[0058] The real-time monitoring and anomaly detection module monitors the sensor data, network connection and system status of IoT devices in real time to detect whether there are data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism is triggered and the management personnel are notified to repair them.

[0059] The loss coefficient calculation and adjustment module calculates the loss coefficient according to the actual paint usage, and dynamically adjusts the paint distribution amount according to the loss coefficient to optimize the use of resources and reduce paint waste.

[0060] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0061] The present invention combines intelligent algorithms with real-time data synchronization mechanisms, so that the amount of paint issued can be accurately matched with actual construction needs, thereby greatly improving the accuracy of paint management. The platform can dynamically predict paint demand based on historical data, construction progress and environmental conditions, and adjust the amount of paint issued in real time. This data-driven intelligent decision can avoid excessive issuance or paint shortages, thereby effectively reducing paint waste and avoiding construction delays caused by insufficient paint. Real-time data synchronization ensures that managers can obtain the latest inventory and consumption information at any time, so that the adjustment of paint issuance can respond to on-site changes in a timely manner, avoiding the problem of inaccurate issuance caused by information lag or untimely updates in traditional manual management. Overall, this intelligent management method improves the efficiency and accuracy of paint issuance, not only optimizes resource allocation, but also enhances the controllability of construction progress.

[0062] The present invention introduces the calculation and optimization distribution mechanism of the paint loss coefficient, so that the platform can adjust the amount of paint distributed in real time during actual use and accurately control the use of paint resources. This mechanism can dynamically adjust the amount of paint distributed according to different construction environments, personnel operations and paint consumption rules to avoid excessive use and waste of paint. For example, when the paint loss coefficient at the construction site is high, the system can automatically increase the paint inventory and distribute it in advance to ensure that there is no shortage of paint supply; when the loss coefficient is low, the system will reduce the amount of paint distributed to effectively control inventory backlogs. Through this refined resource management, the platform can maximize the efficiency of paint use and ensure that every drop of paint can be fully utilized, thereby significantly reducing project costs and reducing paint waste. This not only optimizes the economy of paint use, but also improves the utilization rate of overall construction resources and reduces the environmental burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0064] Figure 1 The present invention is a method flow chart of the method for optimizing the management of the issuance of anti-corrosion coatings based on the Internet of Things.

[0065] Figure 2 This is a module schematic diagram of the anti-corrosion coating distribution optimization management platform based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0066] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0067] The present invention provides Figure 1 The anti-corrosion coating issuance optimization management method based on the Internet of Things includes the following steps:

[0068] Collect paint inventory information, usage, remaining quantity and usage status data in real time through IoT devices, and transmit this data to the cloud data processing platform;

[0069] The specific steps of collecting the inventory information, usage, remaining quantity and usage status data of the coating in real time through the IoT devices and transmitting these data to the cloud data processing platform are as follows:

[0070] IoT devices collect real-time information on paint inventory, usage, remaining quantity, and paint usage status through monitoring equipment;

[0071] Sensors monitor paint consumption, remaining amount and status regularly or continuously, and automatically record every time paint usage or storage changes. These data include paint flow, usage progress and inventory, which can reflect paint consumption in real time. For example, when paint is used, the sensor will update the remaining amount of paint and automatically upload the data through the IoT device. This information is crucial to ensure the real-time nature of the paint supply chain, ensuring that the system always obtains accurate inventory data, thereby avoiding management risks caused by insufficient or excessive use of paint. The real-time and accuracy of data collection provides a solid foundation for the subsequent optimization of paint distribution, while providing real-time data support for construction management personnel to ensure the smooth progress of the project.

[0072] The collected paint usage data is transmitted to the cloud data processing platform via the network;

[0073] In order to ensure fast data transmission and real-time synchronization, efficient communication protocols (such as MQTT, HTTP) are usually used for data upload. After receiving and storing this data through the network, the cloud platform performs further processing and analysis. During data transmission, it is necessary to ensure network stability to prevent data loss or delay due to network interruption. The real-time and accuracy of data transmission are directly related to the timeliness of the platform's analysis results. Therefore, the system will ensure high efficiency and low latency during the transmission process. After receiving the data, the cloud platform stores it in a secure and efficient database for subsequent analysis and use. At the same time, the platform will update and synchronize the data in real time to ensure that the data transmitted by all devices can be summarized in time to prevent data lag or asynchrony, thereby providing accurate information support for subsequent optimization decisions.

[0074] After the data is transmitted to the cloud, the data processing platform receives the paint usage information and stores it;

[0075] Data storage is not just about storing data in a database, but also about organizing, classifying and verifying data to ensure its accuracy and completeness. The platform will timestamp each piece of data and classify and archive it according to different paint types, usage locations or batches. In order to ensure real-time synchronization of data, the cloud platform will use a data synchronization mechanism to ensure that data uploaded by multiple devices can be instantly summarized and reflected on the platform to avoid data lags or omissions. At this time, the real-time synchronization mechanism of the data platform is crucial for paint management, ensuring that the system can obtain the latest paint usage data at any time and conduct corresponding analysis and optimization. The process also needs to ensure data security, avoid data loss or tampering, thereby ensuring the stability and availability of the system, so that managers can make decisions based on the latest data at any time.

[0076] After receiving the paint usage data, the cloud platform will analyze and process the data in real time;

[0077] Through intelligent calculation and algorithm analysis, the platform can intelligently calculate the amount of paint that needs to be issued based on inventory information, construction progress, historical data and other factors. These calculation results can help managers accurately predict paint demand and avoid insufficient or wasted paint. The platform not only provides a match between current inventory and demand, but can also make dynamic adjustments based on construction progress and seasonal factors. The intelligent decision-making function can provide real-time feedback to managers to help them adjust paint procurement plans or construction plans. In this process, the system optimizes calculations based on historical usage data and paint consumption patterns to ensure that the amount of paint issued each time meets actual demand. Through continuous analysis and optimization, the platform can improve resource management efficiency, reduce paint waste, ensure that there is no shortage of paint supply during construction, and optimize the overall use of paint.

[0078] Receive and store coating usage data in the cloud data processing platform to ensure real-time synchronization of data, and process and verify the data to ensure its accuracy and completeness;

[0079] The specific steps to receive and store coating usage data in the cloud data processing platform, ensure real-time synchronization of data, and process and verify the data to ensure the accuracy and completeness of the data are as follows:

[0080] After receiving the paint usage data on the cloud data processing platform, it will first conduct preliminary data verification;

[0081] This process is the first step to ensure the accuracy and reliability of the data. It aims to eliminate any invalid or erroneous data and prevent erroneous data from affecting subsequent analysis. The platform will check whether the data is complete and whether it contains all necessary fields, such as the batch number, inventory, consumption, timestamp, etc. of the paint. The system will automatically check the format of the data to ensure that it meets the preset specifications. If the data format is incorrect or the necessary information is missing, the system will mark it as an exception and notify the management personnel through the alarm mechanism for processing. In addition, the platform will verify whether there are obvious logical errors in the data, such as the paint consumption does not match the actual usage, or the inventory at a certain point in time is unreasonable. This verification process is very important. It can ensure the high quality of the data and provide a reliable basis for subsequent analysis and decision-making.

[0082] The verified data will be stored in the database of the cloud platform;

[0083] In this step, the data will be classified and archived according to different dimensions, such as layered storage by paint type, construction project, batch, and area of ​​use. This classified storage facilitates subsequent quick query and efficient management. The platform will select a suitable storage solution (such as a relational database or a non-relational database) for optimized storage based on the characteristics of the data and business needs to ensure that the data can be quickly accessed even under high concurrency conditions. In addition, the cloud platform will regularly back up data to ensure data security and durability. During the storage process, the platform will also perform data deduplication operations to avoid duplicate records affecting system performance and data accuracy. All stored data will be sorted and archived according to timestamps, so that historical data and current data can be stored in an orderly manner, facilitating future data comparison and trend analysis.

[0084] While storing data, the cloud platform will ensure real-time synchronization of data;

[0085] This means that when new paint usage data is uploaded through IoT devices, the platform automatically updates the database and synchronizes the new data with the existing data. The data synchronization process is critical to keeping the platform data up to date, especially in large-scale project management, where paint usage may change frequently. Real-time synchronization ensures that managers have access to the latest inventory and consumption data at any point in time. The synchronization mechanism usually includes a distributed data synchronization protocol to ensure that data can be updated in real time across multiple servers and databases to prevent data lags or conflicts. The system also regularly checks the integrity of the data to ensure that all uploaded data can be updated to the database without omissions, and to ensure data consistency during the synchronization process to avoid data loss or asynchrony due to network delays or equipment failures.

[0086] After data storage and synchronization are completed, the cloud platform will further process and analyze these paint usage data;

[0087] The platform will first clean the stored paint usage data, remove invalid or erroneous data, and fill in the missing items in the data. Then, the platform will perform intelligent analysis based on historical data and real-time data to identify trends and patterns in paint usage. For example, the platform can calculate information such as the frequency of use of each paint, seasonal fluctuations in usage, and the growth rate of paint consumption. In addition, the platform will use machine learning algorithms to predict data, predict future paint demand, and remind managers to purchase or replenish paint in advance. The platform will also make optimized decisions based on the use of paint to ensure that the amount of paint issued matches the construction needs and avoid waste or shortage of paint. Through continuous analysis and optimization, the cloud platform can provide scientific decision-making support for paint management, thereby improving resource utilization efficiency and management accuracy.

[0088] Intelligently calculate the required paint distribution volume based on paint inventory information, usage status, construction requirements and historical data, and generate an optimized distribution plan to ensure that the paint distribution volume matches actual demand;

[0089] Based on the paint inventory information, usage status, construction requirements and historical data, the required paint distribution volume is intelligently calculated and an optimized distribution plan is generated to ensure that the paint distribution volume matches the actual demand. The specific steps are as follows:

[0090] Based on the processed data, the cloud platform will use intelligent algorithms to calculate the required amount of paint to be distributed;

[0091] The platform will consider a variety of factors for optimization calculations, including the current inventory of paint, changes in construction demand, historical consumption patterns, and the loss coefficient of paint. The system uses intelligent algorithms to analyze the actual needs of the construction site, and combines the current inventory and paint distribution plan to calculate the precise amount of paint distributed. For example, if a large amount of paint is required at a certain construction stage, the system will perform detailed calculations based on the specific requirements of the construction and the time schedule to ensure that the amount of paint required for each link does not exceed the inventory. Intelligent calculations not only consider the current distribution needs, but also comprehensively consider external factors such as the loss ratio of the paint during the construction process and weather, so as to predict the actual usage. In this way, the platform can dynamically adjust the distribution volume to ensure that the distribution volume accurately matches the actual demand.

[0092] Based on the intelligent calculation of the release volume, an optimized paint release plan will be generated;

[0093] This plan not only ensures that the amount of paint distributed is consistent with actual demand, but also comprehensively considers factors such as inventory, transportation, and construction progress to achieve the optimal configuration. The optimization algorithm will develop a reasonable paint distribution schedule based on information such as inventory levels, construction project priorities, and urgent needs. For example, if certain construction areas are about to be completed, the system will adjust the order of paint distribution to ensure that the distribution of paint is in line with the progress of the construction site. At the same time, the distribution plan must also take into account the storage conditions and expiration date of the paint to avoid the expiration or failure of paint stored for a long time. The platform will also continuously optimize the distribution plan based on real-time data from the construction site (such as changes in construction progress, real-time consumption of paint, etc.) to ensure that each distribution of paint can meet construction needs and reduce paint waste.

[0094] After the optimized distribution plan is generated, the cloud platform will dynamically adjust the distribution plan based on the real-time consumption of paint and construction progress;

[0095] During the construction process, the actual amount of paint used may fluctuate due to different construction environments, weather changes, construction personnel operations and other factors. Therefore, the platform will monitor the use of paint in real time, compare it with the initial plan, and adjust the amount of paint issued in a timely manner. For example, when the consumption of paint exceeds expectations, the platform will automatically issue a replenishment request to ensure that the inventory can meet subsequent needs; if the paint consumption is slow, the system will reduce the amount of paint issued and make inventory adjustments in a timely manner. Through this real-time adjustment, the platform can dynamically respond to changes in the construction site and avoid shortages or waste of paint to the greatest extent. This process ensures the accuracy and flexibility of paint management, so that the amount of paint issued is always consistent with the actual construction needs, while improving the efficiency of the use of paint resources.

[0096] Monitor the sensor data, network connection and system status of IoT devices in real time to detect data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism will be triggered and the management personnel will be notified to repair them.

[0097] The sensor data, network connection and system status of IoT devices are monitored in real time to detect whether there are data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism is triggered and the management personnel are notified to repair them. The specific steps are as follows:

[0098] In the IoT system, the sensor data of coating-related equipment must first be monitored in real time. By setting the data collection frequency and time window, ensure that the sensor data can be collected and uploaded to the cloud platform on time. The goal of monitoring is to capture any abnormal data or sensor failures to ensure data accuracy and real-time. By comparing the trends of each sensor's collected data with historical data, the platform can identify whether there are abnormal fluctuations. During the monitoring process, the following formula is used to calculate the degree of data abnormality:

[0099] ,

[0100] In the formula, A(t) represents the abnormality at time point t, D(t) is the sensor data collected at the current time point, is the predicted value based on historical data, σ D is the standard deviation of the historical data;

[0101] If A(t) exceeds the preset threshold λ, the current data is considered abnormal. Through this formula, the system can detect in real time whether the sensor data deviates from the expected range, thereby discovering potential equipment problems or data errors in advance.

[0102] In addition to monitoring sensor data, the network connection status between the device and the cloud platform is also monitored in real time. Network connection problems may cause data transmission delays or losses, affecting the real-time and accuracy of the system. For this reason, the platform will regularly check the connection status of each node to determine the operation of the network. To quantify network performance, the following formula is used to calculate the network stability index;

[0103] ,

[0104] Where S(t) is the network stability index, Δt i is the delay of the i-th data transmission, n is the number of samples;

[0105] If S(t) is lower than the preset stability threshold T, it means that there is a problem with the network connection, which may affect the real-time data transmission. By calculating the stability index, the system can detect bottlenecks or failures in the network connection in a timely manner, ensuring that data can be uploaded to the cloud platform in a timely manner to prevent data loss caused by network problems.

[0106] Sensor fault detection is also crucial. The accuracy and stability of sensors directly affect the quality of data. Therefore, the platform must always monitor the working status of the sensor. If a sensor is abnormal, it must be able to automatically detect and alarm. Sensor anomaly detection uses a model based on historical behavior to calculate the normality index of the sensor status through the following formula;

[0107]

[0108] Where N(t) represents the sensor state normality index at time point t, D(t) is the current sensor reading, μ is the mean of historical data, and α is the adjustment factor;

[0109] If N(t) is lower than a preset threshold v, it means that the sensor is behaving abnormally, and the system will determine that the sensor has failed or deviated from normal working state. Through this algorithm, the platform can quickly identify sensor anomalies and provide data support for subsequent repairs or replacements.

[0110] When data delays, network failures, and sensor anomalies are detected, the alarm mechanism is triggered immediately and the relevant management personnel are notified to handle them. The triggering of the alarm mechanism is based on the threshold set by the system. When any monitoring indicator (such as abnormality, stability index, or sensor normality index) exceeds the set critical value, the system will generate a real-time alarm and inform the management personnel through various means such as email, SMS, or platform notification. In order to improve the accuracy of the alarm, the priority of the alarm is weighted by the following formula:

[0111] P(t)=w1·A(t)+w2·(1-S(t))+w3·(1-N(t))

[0112] , where P(t) is the alarm priority, A(t) is the sensor data anomaly, S(t) is the network stability index, N(t) is the sensor state normality index, w1, w2, w3 are the weight coefficients of sensor data anomaly A(t), network stability index S(t) and sensor state normality index N(t), respectively. These weight coefficients are used to adjust the importance of each monitoring indicator in the alarm priority calculation. Different application scenarios may require different weight configurations;

[0113] Based on the alarm priority, the system will decide whether to take immediate remedial measures or notify management personnel. In this process, the adjustment of the weight coefficient can be optimized according to actual needs to ensure that the system can respond to the most urgent problems first, take remedial measures in time, and prevent major problems in paint distribution management.

[0114] Calculate the loss coefficient based on actual paint usage and dynamically adjust the paint distribution amount based on the loss coefficient to optimize resource usage and reduce paint waste;

[0115] According to the actual paint usage, the loss coefficient is calculated, and the paint distribution amount is dynamically adjusted according to the loss coefficient to optimize the use of resources and reduce paint waste. The specific steps are as follows:

[0116] Before calculating the loss coefficient of the paint, first collect the actual paint usage data, which includes the paint consumption during the construction process, the actual usage area, the remaining amount of paint, the amount of damaged or wasted paint, etc. By real-time tracking and recording the use of the paint, accurate paint consumption data is obtained to calculate the initial loss coefficient. The calculation expression is as follows;

[0117] ,

[0118] In the formula, K loss is the initial loss coefficient, S used is the amount of paint actually used, S expected The expected amount of paint calculated for the system;

[0119] The initial loss factor reflects the difference between the actual and expected consumption of the paint during the construction process. If the loss factor is large, it means that there may be excessive waste or loss of paint during the construction process, and further analysis and adjustment are needed. When collecting data, it is necessary to record the difference between the actual and expected amount of paint used. And perform periodic analysis to ensure the accuracy of the loss factor calculation.

[0120] After calculating the preliminary loss coefficient, the next step is to analyze and identify the various external factors that affect the loss coefficient. The loss of paint is not only determined by the properties of the paint itself, but also affected by various factors such as the construction environment, tools and equipment, personnel operation, and weather. For example, in a high temperature or humid environment, the paint may be wasted due to excessive drying speed or poor fluidity; during the use of the paint, if the construction personnel operate improperly or the construction tools are not suitable, the waste of the paint will also increase. In order to accurately adjust the loss coefficient, external factors are taken into consideration and the impact of these factors on the paint consumption is calculated. By establishing a multi-factor regression model, the influencing factors are quantified and the loss coefficient is further corrected. The form of the model is as follows:

[0121] ,

[0122] In the formula, X j is the jth influencing factor (such as temperature, humidity, operation mode, etc.), β j is the coefficient of each influencing factor, indicating the degree of influence of the influencing factor on the loss. is the adjusted loss coefficient, m is the total number of influencing factors;

[0123] This corrected loss factor can be used to more accurately predict actual paint consumption and provide a basis for adjusting the distribution volume.

[0124] According to the adjusted loss factor The next step is to dynamically adjust the amount of paint issued. The goal of dynamically adjusting the amount of paint issued is to avoid waste or shortage of paint according to the actual loss situation, thereby optimizing the efficiency of paint use. The adjusted amount of paint issued is calculated by the following formula:

[0125] ,

[0126] In the formula, S adjusted is the adjusted issuance amount, S expected is the expected amount of paint used;

[0127] The amount of paint dispensed each time is dynamically adjusted according to the change of the loss coefficient to ensure that the amount of paint dispensed matches the actual demand. If the loss coefficient is high, the system will automatically increase the amount of paint dispensed to ensure that there is no shortage of paint during the construction process; if the loss coefficient is low, the system will reduce the amount of paint dispensed to avoid excessive use of paint. In this way, the amount of paint dispensed can be adjusted in real time according to actual usage and environmental conditions, avoiding waste and improving the efficiency of paint use.

[0128] When the adjusted paint distribution amount is determined, the paint usage at the construction site is monitored in real time to continue to optimize and adjust the distribution amount. If the paint consumption changes significantly during the construction process, feedback data will be collected in real time and the loss coefficient will be recalculated. According to the latest loss coefficient, the future paint distribution plan will be adjusted. For example, if the construction environment changes (such as the weather becomes drier or more humid), the system will adjust the loss coefficient accordingly and increase or decrease the paint distribution amount. This process is achieved through the following dynamic optimization formula:

[0129] ,

[0130] In the formula, is the loss coefficient after dynamic adjustment, and Y is the adjustment factor, which represents the weight of correcting the loss coefficient according to the difference between actual consumption and predicted issuance.

[0131] Through dynamic optimization algorithms, the system can continuously adjust the dispensing strategy based on actual feedback from paint usage to ensure optimal use of paint. As data accumulates and the model continues to improve, the system will predict and adjust the amount of paint dispensed more and more accurately, minimizing waste and improving construction efficiency.

[0132] Implementation method 1: In large-scale construction projects, the demand for paint is usually dynamic. Therefore, ensuring the accurate distribution of paint requires a real-time, efficient dynamic prediction and adjustment mechanism. To meet this challenge, this implementation method integrates intelligent algorithms and data analysis technology to ensure that the amount of paint distributed is highly matched with the actual construction demand.

[0133] First, the platform uses machine learning algorithms (such as regression analysis, neural networks, etc.) to predict demand based on multi-dimensional data such as historical consumption data of paint, construction progress, environmental conditions, and weather changes. The platform automatically calculates the amount of paint required for each stage based on the schedule of the construction plan and the paint consumption patterns at different construction stages. For example, in the early stages of painting, paint consumption may be faster due to the high absorption rate of the construction surface; in the later stages, the paint consumption rate may gradually slow down, and the platform will adjust the issuance amount according to these patterns. In addition, environmental factors such as humidity and temperature will also affect the efficiency of paint use. Therefore, the platform will also integrate weather forecast data to further accurately predict paint demand in the next few days. As the system is continuously optimized, the platform can also feed back the difference between the prediction and actual use to the automatic spraying robot arm. By adjusting the spraying path of the robot arm, the system can optimize the coating efficiency and reduce the loss of paint. At the same time, the system records and quantifies special feature points encountered during the painting process (such as yin and yang corners and irregular areas). Due to their complex shapes, these feature points usually lead to reduced paint coverage or waste of spraying. The platform records these feature points and converts them into data parameters to dynamically correct the paint loss coefficient, thereby further improving the accuracy and efficiency of the paint distribution plan.

[0134] Secondly, the platform continuously tracks the actual use of paint by monitoring the progress of the construction site in real time. After the paint consumption data at the construction site is collected in real time by the IoT device and uploaded to the cloud data processing platform through the wireless network, the system will record the number of each beam section and the corresponding paint usage in real time and store it in the cloud database. This association record of number and usage can provide accurate traceability in the later operation and maintenance process, helping managers to quickly understand the specific paint usage of each beam section, so as to optimize subsequent maintenance work and improve overall management efficiency. The system will not only monitor the inventory of paint in real time, but also dynamically adjust the amount of paint issued according to the construction progress and the work efficiency of the construction personnel. For example, if the construction progress at a certain stage is faster, the system will automatically increase the amount of paint issued; if the paint consumption rate is slower, the system will reduce the amount of paint issued. Through this intelligent adjustment, the platform can maximize the utilization efficiency of paint and avoid waste or shortage of paint.

[0135] The platform also has the ability to seamlessly integrate with construction management systems to synchronize construction tasks and progress changes. Construction tasks often change at any time. For example, the schedule of some construction tasks may be advanced or delayed, which will affect the demand for paint and the distribution plan. The system can quickly recalculate the required amount of paint and adjust the distribution plan based on changes in construction tasks. Assuming that the construction progress of a project is completed ahead of schedule, the system will reduce the distribution of paint based on the amount of work completed ahead of schedule; conversely, if the construction task is delayed, the system will increase the supply of paint in a timely manner to avoid construction stagnation due to insufficient inventory.

[0136] In addition to real-time dynamic prediction of paint consumption, the system also conducts long-term trend analysis of historical data. By analyzing the data of the past few projects, the system can understand the paint consumption patterns at different construction stages and make demand forecasts in advance to avoid waste of resources caused by inaccurate short-term forecasts. In order to further improve the prediction accuracy, the system will continue to optimize and learn itself. As the construction projects proceed, the platform will gradually optimize its prediction model, thereby providing more accurate paint demand forecasts in future projects.

[0137] Through the above method, this implementation plan realizes the intelligent and refined management of paint distribution, so that the distribution of paint can be better matched with actual construction needs, while avoiding excessive or insufficient distribution of paint, thereby optimizing resource allocation, reducing project costs and improving construction efficiency.

[0138] Implementation 2: In order to ensure the accuracy and efficiency of paint distribution, this implementation uses real-time data synchronization and abnormal monitoring mechanisms to ensure system stability and data accuracy. Real-time update and accurate transmission of paint usage data is a key link in optimizing paint distribution management. Any data delay or inaccuracy may lead to resource waste or construction stagnation, so a complete data synchronization and abnormal monitoring solution is necessary.

[0139] IoT devices collect data such as paint inventory, usage, and remaining quantity in real time at the construction site, and upload this data to the cloud platform via wireless networks. In order to ensure the timeliness and integrity of data during transmission, the platform uses efficient communication protocols (such as MQTT or HTTP) to ensure that each piece of data can be quickly transmitted to the cloud. The cloud platform processes and stores data from different devices in real time through a distributed architecture to ensure the timeliness and consistency of the data. To avoid data loss due to network delays or interruptions, the platform has a built-in data caching mechanism to ensure that the uploaded data can be supplemented in time when the network is restored to ensure uninterrupted data synchronization.

[0140] During the data synchronization process, the system will realize the full-process traceability function of the paint's whereabouts, accurately trace the actual use of the paint based on the beam section information, and update the paint usage status of each construction area in real time in combination with the construction progress, paint inventory and consumption data. As the data is continuously updated, the platform will automatically adjust the amount of paint issued to ensure that the paint supply at each stage can accurately match the construction needs. For example, when the paint in a construction area is consumed quickly, the system will update the inventory information in real time and automatically adjust the subsequent issuance plan. If the paint consumption in a certain area is slow, the system will reduce the issuance of paint to avoid excess inventory.

[0141] Through this full-process traceability, the platform can not only optimize the current paint distribution plan, but also provide accurate data support for subsequent operation and maintenance management, further improving resource utilization efficiency and management accuracy.

[0142] In order to ensure the accuracy and timeliness of the paint distribution plan, the cloud platform also integrates an anomaly detection mechanism. This mechanism can monitor the transmission process of paint data and the status of various key data (such as sensor failure, data delay, etc.) in real time. When the data is abnormal, the system will promptly notify the administrator through the alarm mechanism and make corresponding adjustment suggestions. For example, if a paint sensor fails, the platform will automatically detect the data anomaly and mark it as an error, and then start the backup sensor or recalibrate the data acquisition equipment; if the system detects a serious deviation in inventory information, the platform will automatically issue an alarm to prompt the management staff to conduct manual verification.

[0143] Anomaly detection is not limited to the data itself, but also includes monitoring of network status and system health. For example, if the network connection is unstable, it may cause data transmission delays. The platform will promptly detect and adjust the network configuration or replace spare network equipment through system status monitoring. Through this real-time monitoring, the platform can ensure the accuracy and integrity of the data, avoid data problems affecting the paint distribution plan, and thus improve the reliability of the system and construction efficiency.

[0144] The platform's anomaly detection mechanism is not just a passive response, but also has a certain self-healing ability. When the platform finds an anomaly, in addition to alarms and manual intervention, it can also automatically repair the data according to preset rules. For example, when the paint data in a certain area fluctuates due to sensor problems, the system can infer the correct data through algorithms and correct it. In addition, the system can automatically generate feedback reports to help managers understand the root cause of the anomaly in a timely manner, thereby optimizing future data collection and transmission plans.

[0145] Through real-time data synchronization and anomaly detection mechanisms, the implementation method can effectively ensure the accuracy and timeliness of the data, thereby making paint distribution management more precise and stable, and reducing construction risks caused by data errors or system failures.

[0146] Implementation method 3: To further optimize the efficiency of paint use, this implementation method introduces a calculation and optimization mechanism for the paint loss coefficient to ensure that the amount of paint distributed each time matches the actual demand and avoids waste or insufficient paint. The paint loss coefficient is calculated by analyzing historical paint usage data, environmental factors at the construction site (such as temperature and humidity), the speed and path of mechanical equipment, spray gun pressure, flow rate and other factors to calculate the actual paint loss.

[0147] The paint loss coefficient is calculated based on a variety of factors. First, the platform will collect historical usage data of the paint, and by analyzing the paint consumption under different construction conditions, it will deduce the loss coefficients under different construction stages and different climatic conditions. Secondly, the system will dynamically adjust the loss coefficient based on real-time data from the construction site, such as the humidity and temperature of the construction environment, the moving speed and path of mechanical equipment, the pressure of the spray gun, the flow rate, etc. For example, in a high humidity environment, the paint may have a higher volatilization loss, and the system will adjust the amount of paint issued accordingly; if the construction personnel are more skilled in operation, the paint loss coefficient is lower, and the system will reduce the amount issued.

[0148] By calculating the loss coefficient, the platform can accurately estimate the actual amount of paint that needs to be distributed. During the construction process, paint loss is inevitable, so the system will adjust the amount of paint distributed according to the loss coefficient calculated in real time. Assuming that the paint loss coefficient is high, the platform will increase inventory in advance and distribute more paint in advance to ensure that there is no shortage of paint during the construction process; if the loss coefficient is low, the system will reduce the amount of paint distributed to avoid excess inventory.

[0149] During the construction process, the platform will continuously monitor the actual use of the paint and dynamically adjust the distribution plan. For example, if the system finds that the actual consumption of the paint deviates greatly from the expected amount, the platform will promptly calculate the adjusted loss coefficient and re-evaluate the amount of paint distributed. In addition, the platform will also conduct long-term optimization based on the efficiency of paint use and the remaining amount, making the distribution and use of paint more efficient, reducing waste and improving resource utilization. Through this dynamic adjustment based on the loss coefficient, the platform can more accurately control the amount of paint distributed, ensuring that the paint demand at each link is met while avoiding waste.

[0150] By introducing the calculation and optimization mechanism of the paint loss coefficient, the implementation method can greatly improve the accuracy and efficiency of paint use, ensure that the paint is fully utilized throughout the construction process, minimize paint waste, and ensure the smooth progress of construction.

[0151] The present invention combines intelligent algorithms with real-time data synchronization mechanisms, so that the amount of paint issued can be accurately matched with actual construction needs, thereby greatly improving the accuracy of paint management. The platform can dynamically predict paint demand based on historical data, construction progress and environmental conditions, and adjust the amount of paint issued in real time. This data-driven intelligent decision can avoid excessive issuance or paint shortages, thereby effectively reducing paint waste and avoiding construction delays caused by insufficient paint. Real-time data synchronization ensures that managers can obtain the latest inventory and consumption information at any time, so that the adjustment of paint issuance can respond to on-site changes in a timely manner, avoiding the problem of inaccurate issuance caused by information lag or untimely updates in traditional manual management. Overall, this intelligent management method improves the efficiency and accuracy of paint issuance, not only optimizes resource allocation, but also enhances the controllability of construction progress.

[0152] The present invention introduces the calculation and optimization distribution mechanism of the paint loss coefficient, so that the platform can adjust the amount of paint distributed in real time during actual use and accurately control the use of paint resources. This mechanism can dynamically adjust the amount of paint distributed according to different construction environments, personnel operations and paint consumption rules to avoid excessive use and waste of paint. For example, when the paint loss coefficient at the construction site is high, the system can automatically increase the paint inventory and distribute it in advance to ensure that there is no shortage of paint supply; when the loss coefficient is low, the system will reduce the amount of paint distributed to effectively control inventory backlogs. Through this refined resource management, the platform can maximize the efficiency of paint use and ensure that every drop of paint can be fully utilized, thereby significantly reducing project costs and reducing paint waste. This not only optimizes the economy of paint use, but also improves the utilization rate of overall construction resources and reduces the environmental burden.

[0153] The present invention provides Figure 2 The anti-corrosion coating issuance optimization management platform based on the Internet of Things shown in the figure includes a coating data acquisition module, a data receiving and synchronization module, a coating issuance calculation and optimization module, a real-time monitoring and anomaly detection module, and a loss coefficient calculation and adjustment module:

[0154] The paint data collection module collects paint inventory information, usage, remaining quantity and usage status data in real time through IoT devices, and transmits this data to the cloud data processing platform;

[0155] Data receiving and synchronization module receives and stores paint usage data in the cloud data processing platform to ensure real-time synchronization of data, and processes and verifies the data to ensure its accuracy and completeness;

[0156] The paint distribution calculation and optimization module intelligently calculates the required paint distribution volume based on paint inventory information, usage status, construction requirements and historical data, and generates an optimized distribution plan to ensure that the paint distribution volume matches the actual demand;

[0157] The real-time monitoring and anomaly detection module monitors the sensor data, network connection and system status of IoT devices in real time to detect whether there are data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism is triggered and the management personnel are notified to repair them.

[0158] The loss coefficient calculation and adjustment module calculates the loss coefficient according to the actual paint usage, and dynamically adjusts the paint distribution amount according to the loss coefficient to optimize the use of resources and reduce paint waste.

[0159] The method for optimizing the issuance of anti-corrosion coatings based on the Internet of Things provided in an embodiment of the present invention is realized through the above-mentioned anti-corrosion coating issuance optimization management platform based on the Internet of Things. The specific methods and processes of the anti-corrosion coating issuance optimization management platform based on the Internet of Things are detailed in the embodiment of the method for optimizing the issuance of anti-corrosion coatings based on the Internet of Things, which will not be repeated here.

[0160] In addition, this embodiment also provides an electronic device or a computer medium.

[0161] Wherein, the electronic device includes a processor and a memory, and the memory stores computer media. When the computer media is executed by the processor, the anti-corrosion coating issuance optimization management method based on the Internet of Things is run.

[0162] The computer medium stores a computer program, and when the computer program is run by a processor, the method for optimizing the management of the issuance of anti-corrosion coatings based on the Internet of Things is executed.

[0163] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the management of anti-corrosion coating distribution based on the Internet of Things, characterized in that: The following steps are involved: Collect paint inventory information, usage, remaining quantity and usage status data in real time through IoT devices, and transmit this data to the cloud data processing platform; Receive and store coating usage data in the cloud data processing platform to ensure real-time synchronization of data, and process and verify the data to ensure its accuracy and completeness; Intelligently calculate the required paint distribution volume based on paint inventory information, usage status, construction requirements and historical data, and generate an optimized distribution plan to ensure that the paint distribution volume matches actual demand; Monitor the sensor data, network connection and system status of IoT devices in real time to detect data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism will be triggered and the management personnel will be notified to repair them. According to the actual paint usage, the loss coefficient is calculated, and the paint issuance amount is dynamically adjusted according to the loss coefficient to optimize the use of resources and reduce paint waste.

2. The method for optimizing the distribution of anti-corrosion coatings based on the Internet of Things according to claim 1 is characterized in that: The specific steps of collecting the inventory information, usage, remaining quantity and usage status data of the coating in real time through the IoT devices and transmitting these data to the cloud data processing platform are as follows: IoT devices collect real-time information on paint inventory, usage, remaining quantity, and paint usage status through monitoring equipment; The collected paint usage data is transmitted to the cloud data processing platform via the network; After the data is transmitted to the cloud, the data processing platform receives the paint usage information and stores it; After receiving the paint usage data, the cloud platform will analyze and process the data in real time.

3. The method for optimizing the distribution of anti-corrosion coatings based on the Internet of Things according to claim 1 is characterized in that: The specific steps to receive and store coating usage data in the cloud data processing platform, ensure real-time synchronization of data, and process and verify the data to ensure the accuracy and completeness of the data are as follows: After receiving the paint usage data on the cloud data processing platform, it will first conduct preliminary data verification; The verified data will be stored in the database of the cloud platform; While storing data, the cloud platform will ensure real-time synchronization of data; After the data is stored and synchronized, the cloud platform will further process and analyze the paint usage data.

4. The method for optimizing the distribution of anti-corrosion coatings based on the Internet of Things according to claim 1 is characterized in that: Based on the paint inventory information, usage status, construction requirements and historical data, the required paint distribution volume is intelligently calculated and an optimized distribution plan is generated to ensure that the paint distribution volume matches the actual demand. The specific steps are as follows: Based on the processed data, the cloud platform will use intelligent algorithms to calculate the required amount of paint to be distributed; Based on the intelligent calculation of the release volume, an optimized paint release plan will be generated; After the optimized distribution plan is generated, the cloud platform will dynamically adjust the distribution plan based on the real-time paint consumption and construction progress.

5. The method for optimizing the distribution of anti-corrosion coatings based on the Internet of Things according to claim 1 is characterized in that: The sensor data, network connection and system status of IoT devices are monitored in real time to detect whether there are data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism is triggered and the management personnel are notified to repair them. The specific steps are as follows: In the IoT system, the sensor data of coating-related equipment must first be monitored in real time. By setting the data collection frequency and time window, ensure that the sensor data can be collected and uploaded to the cloud platform on time. During the monitoring process, the following formula is used to calculate the degree of data abnormality: ， In the formula, A(t) represents the abnormality at time point t, D(t) is the sensor data collected at the current time point, is the predicted value based on historical data, σ D is the standard deviation of the historical data; In addition to monitoring sensor data, the network connection status between the device and the cloud platform is also monitored in real time. To this end, the platform will regularly check the connection status of each node to determine the operation of the network. To quantify network performance, the following formula is used to calculate the network stability index; ， Where S(t) is the network stability index, Δt i is the delay of the i-th data transmission, n is the number of samples; Sensor fault detection is also crucial. The accuracy and stability of sensors directly affect the quality of data. Sensor anomaly detection uses a model based on historical behavior to calculate the normality index of the sensor status through the following formula: Where N(t) represents the sensor state normality index at time point t, D(t) is the current sensor reading, μ is the mean of historical data, and α is the adjustment factor; When data delays, network failures, and sensor anomalies are detected, the alarm mechanism is triggered immediately and the relevant management personnel are notified to handle them. To improve the accuracy of the alarm, the priority of the alarm is weighted using the following formula: P(t)=w1·A(t)+w2·(1-S(t))+w3·(1-N(t)), Where P(t) is the priority of the alarm, A(t) is the sensor data anomaly, S(t) is the network stability index, N(t) is the sensor state normality index, w1, w2, w3 are the weight coefficients of the sensor data anomaly A(t), the network stability index S(t) and the sensor state normality index N(t), respectively.

6. The method for optimizing the distribution of anti-corrosion coatings based on the Internet of Things according to claim 1 is characterized in that: According to the actual paint usage, the loss coefficient is calculated, and the paint distribution amount is dynamically adjusted according to the loss coefficient to optimize the use of resources and reduce paint waste. The specific steps are as follows: Before calculating the loss coefficient of the paint, first collect the actual paint usage data, and obtain accurate paint consumption data by real-time tracking and recording the paint usage, and calculate the initial loss coefficient. The calculation expression is as follows; ， In the formula, K loss is the initial loss coefficient, S used is the amount of paint actually used, S expected The expected amount of paint calculated for the system; After calculating the preliminary loss coefficient, the next step is to analyze and identify the various external factors that affect the loss coefficient. In order to accurately adjust the loss coefficient, the external factors are taken into account and the impact of these factors on the coating consumption is calculated. By establishing a multi-factor regression model, the influencing factors are quantified and the loss coefficient is further corrected. The form of the model is as follows: ， Where, X j is the jth influencing factor, β j is the coefficient of each influencing factor, is the adjusted loss coefficient, m is the total number of influencing factors; According to the adjusted loss factor The next step is to dynamically adjust the amount of paint to optimize the efficiency of paint use. The adjusted amount is calculated using the following formula: ， In the formula, S adjusted is the adjusted issuance amount, S expected is the expected amount of paint used; When the adjusted paint distribution amount is determined, the paint usage at the construction site is monitored in real time to continue to optimize and adjust the distribution amount. If the paint consumption changes significantly during the construction process, feedback data will be collected in real time and the loss coefficient will be recalculated. According to the latest loss coefficient, the future paint distribution plan will be adjusted. This process is achieved through the following dynamic optimization formula: ， In the formula, is the loss coefficient after dynamic adjustment, and Y is the adjustment factor.

7. An IoT-based anti-corrosion coating issuance optimization management platform, used to implement the IoT-based anti-corrosion coating issuance optimization management method described in any one of claims 1 to 6, characterized in that: It includes paint data acquisition module, data receiving and synchronization module, paint distribution calculation and optimization module, real-time monitoring and anomaly detection module, and loss coefficient calculation and adjustment module: The paint data collection module collects paint inventory information, usage, remaining quantity and usage status data in real time through IoT devices, and transmits this data to the cloud data processing platform; Data receiving and synchronization module receives and stores paint usage data in the cloud data processing platform to ensure real-time synchronization of data, and processes and verifies the data to ensure its accuracy and completeness; The paint distribution calculation and optimization module intelligently calculates the required paint distribution volume based on paint inventory information, usage status, construction requirements and historical data, and generates an optimized distribution plan to ensure that the paint distribution volume matches the actual demand; The real-time monitoring and anomaly detection module monitors the sensor data, network connection and system status of IoT devices in real time to detect whether there are data delays, network failures and sensor anomalies. If there are anomalies, the alarm mechanism is triggered and the management personnel are notified to repair them. The loss coefficient calculation and adjustment module calculates the loss coefficient according to the actual paint usage, and dynamically adjusts the paint distribution amount according to the loss coefficient to optimize the use of resources and reduce paint waste.

8. An electronic device comprises a processor and a memory, wherein the memory stores a computer medium, characterized in that: When the computer medium is executed by the processor, it runs the anti-corrosion coating distribution optimization management method based on the Internet of Things as described in any one of claims 1-6.

9. A computer medium, characterized in that The computer medium stores a computer program, and when the computer program is run by the processor, the method for optimizing the management of the issuance of anti-corrosion coatings based on the Internet of Things as described in any one of claims 1 to 6 is executed.

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