Pipeline integrated layout matching module for closed annular area and maintenance method

Abstract

The invention provides a pipeline integrated layout matching module for a closed annular area and a maintenance method, and the module comprises a mounting column, the bottom end of which is fixed on the ground; the cable bridge is used for erecting a cable, the cable bridge is detachably installed on the installation column, the top of the cable bridge is of an open structure, and the top of the cable bridge is provided with a cover plate used for closing or opening an opening in the top of the cable bridge; the tripod is installed at the position, close to the bottom, of the installation column, the middle of the tripod is used for erecting municipal water pipes and municipal cables, and the tripod is further used for lowering the gravity center of the module.

Description

Technical Field

[0001] This application relates to the field of construction site maintenance equipment technology, and in particular to a pipeline integrated layout module and maintenance method for closed ring areas. Background Technology

[0002] Traditional pipeline layout typically involves multiple redundant main lines and sub-loops, resulting in a high rate of redundant lines and complex layouts. Adding facilities can easily lead to local overload and insufficient local utilization. Maintenance, replacement, and relocation of various professional facilities are extremely difficult. Overhead wires and water pipes are scattered everywhere on site, creating an unsightly and dangerous environment. Therefore, there is an urgent need for a pipeline layout scheme that is simple and efficient in macroscopic layout and orderly in local layout. Summary of the Invention

[0003] The purpose of this invention is to solve the above-mentioned problems by providing a pipeline integrated layout module and maintenance method for closed ring areas.

[0004] The technical solution of this application is implemented as follows:

[0005] In a first aspect, this application provides a pipeline integration and deployment module for a closed ring-shaped area, the module comprising:

[0006] The mounting column is fixed to the ground at its bottom.

[0007] A cable tray for laying cables, the cable tray being detachably mounted on the mounting post, the top of the cable tray having an open structure with a cover plate for closing or opening the opening at the top of the cable tray;

[0008] A tripod is installed near the bottom of the mounting column. The middle part of the tripod is used to support the water pipe, and the tripod is also used to lower the center of gravity of the module.

[0009] The modules can be deployed around the construction site to form a modular enclosure surrounding the site, wherein the cable tray is located near the inside of the modular enclosure, the cables on the cable tray are the main network, and the cables connecting to electrical appliances from the cable tray are the branch lines.

[0010] The module further includes:

[0011] Electrical connection points are used to relocate municipal cables into the central control room.

[0012] Central control room, where cables are moved into the cable trays;

[0013] A water connection point is used to move municipal water pipes into the tripod.

[0014] The advantages or beneficial effects of the above technical solutions include at least the following:

[0015] Secondly, this application also provides a maintenance method for maintaining the pipeline integrated layout module for a closed ring area as described above; the method includes:

[0016] A humidity sensor is installed in the cable tray to collect humidity data and sealing status information inside the cable tray and generate an initial environmental parameter record.

[0017] Based on the comparison between the humidity data in the initial environmental parameter record and the preset threshold range, the acquisition frequency of the sensor device is adjusted to obtain dynamically adjusted humidity data;

[0018] By using time series analysis algorithms, the changing trend of the dynamically adjusted humidity data is analyzed to identify humidity anomalies.

[0019] Based on the sealing status information and the humidity anomaly points, a decision tree algorithm is used to determine the sealing integrity inside the modular enclosure frame and generate a sealing status assessment result.

[0020] The abnormal state features in the sealing state assessment results are classified using a clustering analysis algorithm to obtain the abnormal state classification results.

[0021] Based on the abnormal state classification results, an environmental parameter adjustment instruction is generated and sent to the control unit of the modular enclosure frame to adjust the environmental parameters inside the modular enclosure frame and generate an optimized environmental parameter record.

[0022] The optimized environmental parameter records are used to update the initial environmental parameter records, which are then stored in the data recording system to generate the final environmental parameter dataset.

[0023] The advantages or beneficial effects of the above technical solutions include at least the following:

[0024] (1) Convenience: The assembly is simple and the deployment is efficient. All equipment is installed through mushroom-shaped pins and gravity mounting holes. After aligning with the insertion hole, it can be reliably fixed by gravity. The same applies to unloading the equipment. The orderly arrangement of the lines facilitates maintenance and the addition or removal of branches. It solves the problems of traditional fixed wiring facilities being difficult to dismantle, modify, repair and replace, and the cumbersome installation of temporary wiring boxes and water tanks. It greatly increases the flexibility of the lines and saves labor and material consumption.

[0025] (2) Aesthetics: All pipelines and equipment are arranged in an orderly manner and labeled, which greatly improves the quality of the construction site. There will be no more messy and dangerous dense flying wires on site.

[0026] (3) Economic efficiency:

[0027] The loop design significantly improves pipeline utilization, saves materials and reduces the need for individual supports and hangers; its modular, split design allows for easy disassembly and installation, greatly reducing disassembly and modification costs; its integrated design simultaneously fulfills the functions of fencing and multi-disciplinary supports and hangers, saving the cost of fencing and separate fixed supports and hangers for each discipline; and its space-saving integrated design only requires the area outside the fence boundary to free up the entire internal space, improving area utilization and economy.

[0028] (4) Adaptability: It can be adapted to various types of pipeline layouts, can accommodate various professional pipelines and equipment, and the universal bayonet can integrate most installations.

[0029] (5) Safety: Although the various specialties are integrated, the space is separated vertically. The water pipes are placed below and the power cables are raised, which not only avoids the power cables from being soaked in water, but also lowers the center of gravity of the system, greatly increasing the stability compared to traditional linear enclosure facilities. External equipment or branch pipelines led out from the outside can be directly inserted through the installation holes of the installation column, directly and completely concealed into the next pipeline loop or external electrical appliances, achieving full protection against rain, dust and sun exposure, greatly reducing the risk of short circuits and aging of the lines, and significantly improving safety. Attached Figure Description

[0030] The accompanying drawings illustrate exemplary embodiments of the present application and, together with the description thereof, serve to explain the principles of the present application. These drawings are included to provide a further understanding of the present application and are incorporated in and constitute a part of this specification.

[0031] Figure 1 A schematic diagram of the module structure according to an embodiment of the present invention is shown;

[0032] Figure 2 A schematic diagram of a cover plate and cable tray according to an embodiment of the present invention is shown;

[0033] Figure 3 A schematic diagram of the fit between the mushroom-shaped pin and the mounting hole according to an embodiment of the present invention is shown;

[0034] Figure 4 A schematic diagram of the tripod structure according to an embodiment of the present invention is shown. Figure 1 ;

[0035] Figure 5 A schematic diagram of the tripod structure according to an embodiment of the present invention is shown. Figure 2 ;

[0036] Figure 6 A schematic diagram of the module layout according to an embodiment of the present invention is shown;

[0037] Figure 7 A flowchart illustrating the maintenance method according to an embodiment of the present invention is shown;

[0038] Reference numerals: 10, mounting post; 11, mounting hole; 111, large hole; 112, vertical groove; 20, cable tray; 21, cover plate; 30, tripod; 31, first right-angled side; 32, second right-angled side; 33, hypotenuse; 40, mushroom-shaped pin; Detailed Implementation

[0039] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0040] It should be noted that, where there is no conflict, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0041] It should be understood that the term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this application are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0042] It should be noted that the terms "one" and "more" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0043] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0044] A pipeline integration and deployment module for enclosed ring-shaped areas, the module comprising:

[0045] Mounting column 10, with its bottom end fixed to the ground;

[0046] Cable tray 20 is used for laying cables. Cable tray 20 is detachably mounted on mounting post 10. The top of cable tray 20 has an open structure, which allows cables to be placed inside the cable tray 20. The top of cable tray 20 has a cover plate 21 for closing or opening the opening at the top of cable tray 20. When cables need to be used, simply open the cover plate 21 and remove the cables from the cable tray 20.

[0047] The tripod 30 is installed near the bottom of the mounting column 10. The middle part of the tripod 30 is used to lay municipal water pipes and municipal cables. The tripod 30 is also used to lower the center of gravity of the module. Specifically, the heavier municipal water pipes are placed in the tripod 30 to increase the weight of the tripod 30. In addition, the tripod 30 can also increase the bearing area of ​​the mounting column 10 with the ground, reducing the risk of the mounting column 10 collapsing due to the excessive weight of the cable tray 20.

[0048] The tripod 30 described above is a right-angled tripod 30, which has a first right-angled side 31, a second right-angled side 32, and a hypotenuse 33, wherein the length of the first right-angled side 31 is greater than the length of the second right-angled side 32; the first end of the first right-angled side 31 is fixed to the ground, the first end of the second right-angled side 32 is fixed to the mounting post 10, and the other end is fixed to the second end of the first right-angled side 31; the first end of the hypotenuse 33 is fixed to the mounting post 10, and the second end is fixed to the first end of the first right-angled side 31; so that the hypotenuse 33 of the right-angled tripod 30 faces the mounting post 10, thereby forming a triangular area between the tripod 30 and the mounting post 10, which enhances the stability of the mounting post 10.

[0049] The module further includes a enclosure panel installed between the mounting posts 10 to enable the module to function as an enclosure.

[0050] Based on the above, such as Figure 6 As shown, the modules can be deployed around the construction site to form a modular enclosure surrounding the site. The cable tray 20 is located near the inside of the modular enclosure. The cables on the cable tray 20 are the main network, and the cables that connect to electrical appliances from the cable tray 20 are the branch lines.

[0051] The cables are used to connect the main pipelines for power, water supply, and drainage equipment. The cables are introduced from the primary connection node and transported to external equipment and individual buildings via a main pipeline network arranged within the modular perimeter fencing. External equipment or branch lines extending from the perimeter can also be directly inserted through the mounting holes 11 of the mounting columns 10, allowing for completely concealed insertion into the next pipeline loop or external electrical appliances. This ensures complete protection against rain, dust, and direct sunlight, significantly reducing the risk of short circuits and aging, and substantially improving safety.

[0052] The module further includes:

[0053] Electrical connection point, used to move municipal cables into cable tray 20; central control room, used to move cables into cable tray 20; water connection point, used to move municipal water pipes into tripod 30.

[0054] Furthermore, the side of the mounting post 10 is provided with several mounting holes 11 at equal intervals along its height direction, and the several mounting holes 11 form a row, with two rows of mounting holes 11 on one mounting post 10.

[0055] like Figure 1 As shown in the enlarged view on the right, the mounting hole 11 includes a large hole 111 and a vertical groove 112;

[0056] A mushroom-shaped pin 40 is installed on the cable tray 20. The end of the mushroom-shaped pin 40 has a fixing head that is slightly smaller than the hole, so that the mushroom-shaped pin 40 can be inserted into the large hole 111. Specifically, the fixing head is inserted into the hole and moves downward along the vertical groove 112, so that the end of the mushroom-shaped pin 40 is locked inside the mounting post 10, so that the two sides of the cable tray 20 are fixedly installed on the two mounting posts 10.

[0057] The module further includes a fire hydrant, on which a mushroom-shaped pin 40 is installed. The fire hydrant can be installed on the mounting column 10 via the mushroom-shaped pin 40; the fire water pipe of the fire hydrant can be erected via a tripod 30.

[0058] Embodiments of the present invention also provide a maintenance method for maintaining the pipeline integrated layout module for a closed ring area as described above; the method includes:

[0059] A humidity sensor is installed in the cable tray 20 to collect humidity data and sealing status information inside the cable tray 20, generating an initial environmental parameter record. The method for maintaining the sealing status is as follows: Since the cable tray 20 is a sealed space, the humidity inside is low, and there is a large difference between the humidity inside and outside the environment. If the seal fails, external moisture will enter, causing the internal humidity to rise. When the humidity reaches a threshold, the sealing status is considered abnormal. It should be noted that if some covers 21 of the cable tray 20 are opened for cable use, the humidity sensor in that area will be turned off.

[0060] By deploying humidity sensors inside the cable tray, humidity data and seal status information inside the enclosure are collected to generate an initial environmental parameter record. If the humidity data exceeds the preset threshold range, the acquisition frequency is adjusted through the sensor device to obtain the dynamically adjusted humidity data. Based on the dynamically adjusted humidity data, a time series analysis algorithm is used to analyze the humidity change trend and determine the humidity anomaly points. Through the seal status information, combined with the humidity anomaly points, a decision tree algorithm is used to judge the seal integrity inside the enclosure and generate a seal status evaluation result. Abnormal state features are extracted from the seal status evaluation result, and a clustering analysis algorithm is used to classify the types of abnormal states to obtain an abnormal state classification result. According to the abnormal state classification result, an environmental parameter adjustment instruction is generated and sent to the control unit of the modular enclosure framework to adjust the environmental parameters inside the enclosure and generate an optimized environmental parameter record. Through the optimized environmental parameter record, the initial environmental parameter record is updated and stored in the data recording system to obtain the final environmental parameter dataset.

[0061] Specifically, the specific implementation method of integrating the sensor device through the pre-established modular enclosure framework to collect the humidity data and seal status information inside the enclosure and generate an initial environmental parameter record is as follows: First, the modular enclosure framework uses standardized aluminum alloy modules with a size of 2 meters × 1.5 meters. There are sensor installation slots reserved inside the framework, with a slot depth of 0.05 meters and a slot width of 0.02 meters to ensure that the sensors are firmly embedded. The sensor device selects DHT22 humidity sensors and airtightness detection sensors. The measurement range of DHT22 is 0 - 100%RH, with an accuracy of ±2%RH. The airtightness sensor detects the pressure difference, with a range of 0 - 500Pa and an accuracy of ±1Pa. The sensors are connected to the microcontroller (ESP32) through the I2C protocol, and the data acquisition frequency is once every 5 seconds. The microcontroller stores the acquired data in the local SD card and uploads it to the cloud database through the Wi-Fi module. The humidity data processing uses the moving window average algorithm, with a window size of 10 acquisitions (50 seconds). The calculation formula is H_avg = (H1 + H2 +... + H10) / 10, where H is the humidity value in %RH, and abnormal values (|H - H_avg| > 5%RH) are excluded. The seal status is analyzed through the pressure difference. The threshold P0 = 50Pa is set. If the detected value P < P0, it is determined that the seal is good, otherwise it is marked as a leakage risk. Data analysis uses Python scripts, and the Pandas library is called to process the time series data to generate an initial environmental parameter record, including the timestamp, average humidity, and seal status (0 represents normal, 1 represents abnormal).

[0062] For example, a data collection might show a humidity level of 75.2% RH and a pressure difference of 45 Pa. After algorithm processing, H_avg = 75.0% RH, P < 50 Pa, recorded as {"Time": "2025-07-15 01:46:00", "Humidity": 75.0", "Sealing Status": 0}. The cloud database uses MySQL for storage, and the table structure includes fields: Time (DATETIME), Humidity (FLOAT), and Sealing Status (INT). To ensure logical rigor, if the humidity exceeds 80% RH five consecutive times or the sealing status is abnormal, the system automatically triggers an alarm and pushes it to the management terminal via the MQTT protocol. This process requires no manual intervention, is fully automated, and the data analysis and record generation logic is clear and closely linked to the perimeter environmental monitoring business.

[0063] Based on the initial environmental parameter records, the humidity data is classified and organized using a data processing module to determine whether there are any abnormal values ​​exceeding the preset threshold, thereby obtaining a humidity anomaly indicator.

[0064] Humidity data is extracted from initial environmental data records and preliminarily processed using a data processing module to obtain a categorized humidity dataset. Based on this dataset, each data set is compared to a preset threshold. If a data set's humidity value exceeds the threshold, it is marked as potentially abnormal, thus identifying a set of potential abnormal data. A support vector machine algorithm is used to further analyze this set, combining historical fluctuations of environmental parameters to determine if genuine humidity anomalies exist, resulting in preliminary anomaly identification. Using these preliminary anomaly identifications, related environmental parameter records are retrieved, and the correlation between humidity anomalies and environmental parameters is analyzed to determine the accuracy of the anomaly identifications. Based on the identified anomaly identifications, corresponding humidity data and environmental parameters are extracted. Time series analysis is used to detect the persistence and trend of humidity anomalies, acquiring anomaly change characteristics. Based on these characteristics and historical data from the data module, the frequency and distribution patterns of anomalies are analyzed to determine if the anomalies are periodic, yielding the final humidity anomaly analysis results. These final results generate corresponding anomaly classification labels, which are stored in the data module for subsequent dynamic monitoring and adjustment of environmental parameters.

[0065] Specifically, based on the initial environmental parameter records, assuming the humidity data consists of a series of sensor-collected values ​​in percentage form, recorded as [45.2, 48.7, 50.1, 62.3, 75.8, 82.4, 49.6, 47.9]. First, the data processing module classifies and organizes the humidity data using the K-means clustering algorithm (K=3), dividing the data into three categories: low, medium, and high humidity. The distance from each data point to the initial random centroid is calculated using Euclidean distance, and the centroid is iteratively updated until convergence, yielding the classification results: low humidity (45.2, 47.9, 48.7, 49.6, mean 47.35), medium humidity (50.1, 62.3, mean 56.2), and high humidity (75.8, 82.4, mean 79.1). Next, outliers are identified, with preset humidity thresholds of an upper limit of 80% and a lower limit of 40%. An anomaly detection algorithm is used to calculate the deviation of each data point from the mean of its category, with a threshold set at twice the standard deviation. The standard deviation for the low humidity category is 1.82, with a deviation range of [43.71, 50.99], all data are normal; the standard deviation for the medium humidity category is 6.1, with a deviation range of [44.0, 68.4], data are normal; the standard deviation for the high humidity category is 3.25, with a deviation range of [72.6, 85.6]. 82.4 exceeds the 80% threshold and is marked as an anomaly. Humidity anomaly markers are generated: 82.4 is marked as 1 (anomaly), and the rest as 0 (normal). To establish business correlation, the anomaly markers can be transmitted to the environmental control system. If the marker is 1, ventilation equipment is triggered to reduce humidity, ensuring environmental stability. The entire process is automatically executed through the data processing module. The algorithm logic is rigorous, and the classification and anomaly detection results are based on numerical calculations, ensuring the objectivity and accuracy of the analysis.

[0066] If the humidity anomaly indicator shows that the humidity exceeds the standard, the abnormal information will be sent to the management collaboration platform through the data transmission channel to obtain the location information of the corresponding enclosure module.

[0067] Humidity data is monitored in real time using data acquisition equipment. If the detected humidity data exceeds a preset range, a corresponding anomaly information record is generated. Based on the generated anomaly information record, the information is uploaded to the management and collaboration platform through a pre-established data transmission channel, completing the initial transmission of the anomaly information. The location data of the fencing modules associated with the anomaly information is obtained from the management and collaboration platform to determine the specific area where the anomaly occurred. For the obtained fencing module location data, a preset decision tree model is used to classify and judge the severity of the anomaly information, resulting in an anomaly level classification result. Based on the anomaly level classification result, if the level is higher than a preset threshold, a positioning calibration command is sent to relevant devices through the management and collaboration platform to obtain more accurate fencing module location data. By analyzing the accurate location data and combining it with the changing trend of humidity data, it is determined whether the anomaly has a risk of spreading, and whether to trigger the linkage response mechanism. If the anomaly is determined to have a risk of spreading, an early warning signal is sent to surrounding fencing modules through the management and collaboration platform, and the anomaly information and location data are simultaneously updated to relevant systems.

[0068] Specifically, when the humidity sensor detects that the ambient humidity exceeds a set threshold, such as a relative humidity greater than 85%, the system will automatically trigger an anomaly flag. The humidity sensor collects data every 10 seconds, comparing the current humidity value with the preset threshold of 85%. If three consecutive data collections exceed the threshold, an anomaly is confirmed. The anomaly information includes the humidity value, collection time (e.g., 2025-07-15 01:45:00), and sensor ID (e.g., S001), which is sent to the management and collaboration platform via the MQTT protocol through a data transmission channel. AES-128 encryption is used during transmission to ensure data security. After receiving the anomaly information, the management and collaboration platform parses the data packet, extracts the sensor ID, and queries the pre-stored sensor location database. The database is stored in JSON format and contains fields such as {“sensor_id”:“S001”, “enclosure_module”:“M01”, “location”:“North Area Enclosure No. 1”}. The system retrieves the location information of the corresponding enclosure module by performing an SQL query: `SELECT enclosure_module, location FROM sensor_map WHERE sensor_id = 'S001'`. If the query result is empty, the system deduces the nearest enclosure module based on the prefix rule of the sensor ID (e.g., S001 belongs to the North Zone). The derivation algorithm is a hash mapping based on the sensor ID, calculated as `hash(sensor_id) % module_count`. Assuming `module_count = 10`, the module number M01 is obtained. Finally, the system integrates the anomaly information with the location information to form a JSON message: `{"humidity":87.5, "time":"2025-07-15 01:45:00", "sensor_id":"S001", "location":"North Zone Enclosure No. 1"}`. This message is pushed to the monitoring terminal via a RESTful API for subsequent processing. The entire process is automated, requiring no manual intervention, ensuring real-time performance and accuracy.

[0069] Based on the obtained location information of the enclosure module, a standardized interface is called to extract the sealing status data of the module and determine whether there is a potential risk of sealing failure.

[0070] Raw data is obtained from the location information of the enclosure modules through a standardized interface. A preliminary sealing status dataset is obtained using a pre-defined parsing method. Based on this dataset, features are extracted for each enclosure module's sealing status information. A support vector machine (SVM) algorithm is used to classify these features, determining the initial presence of any abnormal states. If the preliminary assessment indicates an abnormal state, historical sealing status data is obtained for the abnormal enclosure modules. Time series analysis is used to determine the persistence and trend of the abnormal state. Based on the trend analysis, for enclosure modules with persistent abnormalities, their location information and surrounding environmental data are extracted. By comparing these data with a pre-defined threshold, the sealing failure risk level under environmental influence is determined. If the sealing failure risk level under environmental influence exceeds the pre-defined threshold, detailed sealing status information and module detection records are obtained for high-risk enclosure modules to determine the specific distribution of failure risk points. Based on the distribution of failure risk points, a data fusion method is used to combine location and status information for the sealing status information of high-risk enclosure modules to determine the final potential threat level.

[0071] Specifically, based on the acquired location information of the fencing modules, the system first automatically calls the geographic information system interface to extract the specific coordinate data of the modules. For example, if a module's location is longitude 116.3975 and latitude 39.9088, this coordinate data is mapped to the corresponding device number, such as SM-2023-001. Then, the system calls a standardized interface to connect to the device status database and extracts the module's sealing status data. Assuming the obtained sealing pressure value is 3.2 MPa, while the standard threshold is 3.5 MPa, and the humidity sensor data is detected at 75%, exceeding the normal range of 60% (the upper limit), the system will first extract the module's sealing status data. Next, the system uses a preset seal failure risk assessment algorithm for analysis. The algorithm formula is: Risk Index = (Standard Pressure Value - Actual Pressure Value) / Standard Pressure Value * 100 + Humidity Exceedance Percentage * 0.5. Substituting the data, the calculated risk index is (3.5 - 3.2) / 3.5 * 100 + (75 - 60) * 0.5 = 8.57 + 7.5 = 16.07. Assuming a risk threshold of 15, the risk index of this module exceeds the threshold, indicating a potential risk of seal failure. To form a complete logical chain, the system further correlates with business data, comparing the risk assessment results with historical maintenance records. It was found that the module's last maintenance was 6 months ago, exceeding the recommended 3-month maintenance cycle, suggesting that the long-term lack of maintenance may be one of the reasons for the pressure drop and humidity exceeding the standard. Finally, the system automatically generates a risk report, including location, seal data, risk index, and maintenance recommendations, and pushes it to the relevant management system to ensure automated connection of subsequent processing flows. Through the above process, from location acquisition to risk assessment and then to business correlation, a tight logical closed loop is formed, ensuring the comprehensiveness and accuracy of the technical analysis.

[0072] By analyzing the sealing status data and combining it with the environmental adaptation model, we can determine whether the module is adapted to the current external environmental conditions and obtain the environmental adaptability assessment results.

[0073] Data on sealing status and external environmental conditions are acquired through sensors. Data preprocessing methods are used to denoise and standardize the collected data, resulting in processed sealing status and environmental condition datasets. Based on these datasets, a support vector machine (SVM) algorithm is employed to classify the data and assess the module's initial adaptability under the current environmental conditions, yielding classification results. If the classification results indicate insufficient adaptability, the environmental change trends in the environmental condition dataset are analyzed to extract key environmental parameters, resulting in an environmental change feature set. Based on this feature set, a linear regression algorithm is used to optimize the environmental adaptation model, updating the model parameters to obtain an optimized model. Using the optimized model and the processed sealing status dataset, the module's adaptability under the current external environmental conditions is reassessed, resulting in an updated adaptability assessment result. If the updated assessment result meets a preset threshold, the module is deemed to be adapted to the current environmental conditions, and the final adaptability result is output. If not, abnormal parameters in the environmental change trends are extracted, resulting in an abnormal environmental parameter set. Based on this set, the module's performance parameters are adjusted, new sealing status data is generated, and data preprocessing and adaptability assessment are re-executed to obtain the final adaptability result.

[0074] Specifically, by analyzing the sealing status data and combining it with the environmental adaptation model, we determine whether the module is adapted to the current external environmental conditions and obtain the environmental adaptability assessment results. First, let's assume we obtain sealing status data from the module's sensors, including a sealing pressure of 3.5 MPa, a temperature of 45°C, and a humidity of 70%. Using a data processing algorithm, we standardize this data and calculate the deviation value of the sealing pressure using the formula: Deviation value = |Actual value - Standard value| / Standard value. Assuming the standard sealing pressure is 3.0 MPa, the deviation value is |3.5 - 3.0| / 3.0 = 0.1667, indicating that the pressure is 16.67% too high. Next, we input the temperature and humidity data into the environmental adaptation model, which is built based on historical data and uses a weighted scoring algorithm. The weight for temperature is 0.4, the weight for humidity is 0.3, and the weight for sealing pressure is 0.3. We calculate the comprehensive adaptability score using the formula: Score = (1 - Temperature deviation rate) × 0.4 + (1 - Humidity deviation rate) × 0.3 + (1 - Pressure deviation rate) × 0.3. Assuming a standard temperature of 40℃ and a standard humidity of 60%, the temperature deviation rate is |45-40| / 40 = 0.125, and the humidity deviation rate is |70-60| / 60 = 0.1667. The overall score is (1-0.125)×0.4 + (1-0.1667)×0.3 + (1-0.1667)×0.3 = 0.35 + 0.25 + 0.25 = 0.85. If the preset adaptability threshold is 0.8, a score of 0.85 is greater than the threshold, indicating that the module adapts to the current environment. Subsequently, the system automatically generates an evaluation report, records the deviation rates and overall score, and compares them with historical data to analyze trends. If the score is below 0.8 for three consecutive times, an early warning mechanism is triggered, and the associated business system adjusts the module parameters, such as reducing operating power by 10%, to ensure long-term stability. Through the above analysis and calculation, a complete logical chain is formed from data collection to evaluation and then to business linkage, ensuring the reliable operation of the module in different environments.

[0075] Based on the environmental adaptability assessment results, if the module is determined to be unsuitable for the current environment, the cable protection mechanism is triggered, generating protection commands and transmitting them to the relevant modules.

[0076] By collecting environmental adaptability data, the module's operational status information in the current environment is obtained to determine whether it meets the adaptation requirements. If the collected operational status information indicates that the module is incompatible with the current environment, the necessity of activating the cable protection mechanism is determined based on preset trigger conditions. For the determined protection mechanism requirements, corresponding protection commands are generated using pre-established command templates to obtain specific operation content. Key operation content is extracted from the generated protection commands and transmitted to the corresponding module, obtaining status feedback information during transmission. Based on the status feedback information during transmission, it is determined whether the command was successfully delivered to the corresponding module. If it was not successfully delivered, the protection command is regenerated and transmitted. The execution status of the corresponding module after receiving the command is obtained, and the cable protection mechanism is determined to be operating normally by analyzing the execution status data. If the execution status data indicates that the protection mechanism is malfunctioning, the protection command content is adjusted according to the environmental assessment results and transmitted to the corresponding module again.

[0077] Specifically, during the environmental adaptability assessment, the system first collects current environmental data through sensors, such as temperature, humidity, and wind speed. Assuming the temperature is 45 degrees Celsius, humidity is 85%, and wind speed is 10 meters per second, exceeding the module's designed operating range (temperature limit 40 degrees Celsius, humidity limit 80%, wind speed limit 8 meters per second), the system inputs this data into a preset environmental adaptability assessment model. This model uses a weighted scoring algorithm, with temperature weighted at 0.4, humidity weighted at 0.3, and wind speed weighted at 0.3. The calculated comprehensive score is: (45-40) / 5*0.4 + (85-80) / 5*0.3 + (10-8) / 2*0.3 = 0.8 + 0.3 + 0.3 = 1.4. Since the score exceeds the threshold of 1.0, the module is determined to be unsuitable for the current environment. The cable protection mechanism is then triggered, and the system automatically generates a protection command. This command includes reducing the module's operating power to 50% to reduce overheating risk and activating the cable cooling system, setting the cooling power to 75 watts for 30 minutes. After generation, the command is transmitted to the relevant module via an internal communication protocol (such as CAN bus) at a transmission rate of 1 Mbps, ensuring delivery within 100 milliseconds. During transmission, the system monitors data packet integrity in real time, using a CRC checksum algorithm. If an error is detected, automatic retransmission is performed, with a maximum of three retries to ensure command accuracy. This process forms a closed-loop logic, from environmental data acquisition to command execution, with each step interconnected. It is also linked to module operating status monitoring. If the temperature remains above 40 degrees Celsius after cooling, the system will further adjust the power to 30% until the environmental adaptability score falls below the threshold, ensuring safe equipment operation.

[0078] In response to the generated protection commands, the internal protection settings of the module are adjusted through a real-time feedback channel, and the adjusted status data is recorded.

[0079] Protection commands are obtained from the real-time feedback channel, and their content is parsed to obtain a command parameter set. If the command parameter set conforms to a preset format standard, the module's protection settings are updated according to the parameter set, generating an adjusted protection configuration. Using the adjusted protection configuration, the module's protection status value is calculated and stored in the status database to obtain a protection status record. The protection status record is retrieved from the status database, and the status change trend in the record is analyzed to generate a status analysis result. If the status analysis result exceeds a preset threshold range, an alarm signal is sent through the real-time feedback channel to determine the alarm trigger status. Based on the alarm trigger status, the priority setting of the real-time feedback channel is adjusted to optimize command transmission efficiency, resulting in an updated channel configuration. Using the updated channel configuration, protection commands are retrieved again, and the above steps are executed repeatedly to generate continuously optimized protection status data.

[0080] Specifically, in response to the generated protection commands, the internal protection settings of the module are adjusted through a real-time feedback channel, and the adjusted status data is recorded. The specific implementation method can be integrated into a coherent systematic process. First, assuming that the protection command is generated by the core control system based on the environmental threat level, for example, the current threat level is level 3 (out of 5), the system automatically generates a command requiring the energy barrier strength of the protection module to be increased to 80%. This command is transmitted to the protection module through a high-speed data channel with a transmission rate of 100Mbps to ensure the real-time nature of the command. Next, after receiving the command, the embedded processor inside the protection module adjusts the parameters based on a preset algorithm: New strength value = Current strength value + (Target strength value - Current strength value) × 0.5. Assuming the current strength is 60%, then the new strength value = 60 + (80 - 60) × 0.5 = 70%. This gradual adjustment method avoids system instability caused by sudden energy fluctuations. Subsequently, the real-time feedback channel collects energy fluctuation data during the adjustment process, sampling 10 times per second. If the fluctuation range is detected to be within ±2%, the adjustment process is deemed stable, and the data is sent back to the core system for analysis. The analysis process uses a mean filtering algorithm to calculate the average fluctuation value of the most recent 5 seconds as 1.2%, which is lower than the threshold of 1.5%, confirming that the adjustment meets expectations. Finally, the system stores the adjusted status data (e.g., intensity value 70%, fluctuation value 1.2%) in a distributed database in JSON format, including timestamps, adjustment values, and fluctuation parameters, ensuring subsequent traceability. Simultaneously, blockchain technology is used to encrypt and back up the data to prevent tampering. This process forms a closed loop, from instruction generation to status recording, with each step interconnected. If the database storage space is insufficient, data compression can be automatically triggered, with a compression rate set to 50%, ensuring continuous system operation and logically tightly linking protection adjustment and data management.

[0081] Based on the adjusted status data, humidity data and sealing status information are re-collected to determine whether the environment has returned to the normal range, and updated environmental parameter records are obtained.

[0082] Status data, including humidity and sealing status information, is acquired through monitoring equipment. If the status data is complete, a sensor calibration algorithm is used to preprocess the humidity data to obtain calibrated humidity data. Key features are extracted from the calibrated humidity data, and a K-means clustering algorithm is used to classify the humidity data to determine the classification result. If the humidity data classification result exceeds the preset normal range, it is marked as abnormal humidity data, and an abnormal status identifier is generated. Based on the abnormal status identifier and sealing status information, a decision tree algorithm is used to analyze whether the sealing status affects the humidity anomaly, obtaining a sealing impact analysis result. If the sealing impact analysis result shows that the sealing status is abnormal, an adjustment command is generated to update the sealing status data. Humidity data is re-acquired using the adjusted sealing status data to obtain updated humidity data. If the updated humidity data returns to the normal range, a recovery confirmation signal is generated. Based on the recovery confirmation signal and the updated humidity data, environmental parameter records are generated. The environmental parameter records are verified through data analysis to obtain updated environmental parameter records. Key parameters are extracted from the updated environmental parameter records, stored in a database, and the final environmental status data is generated.

[0083] Specifically, based on the adjusted status data, the system first re-collects humidity data and sealing status information through the sensor network. Assuming a DHT22 sensor is used to collect ambient humidity, with a sampling frequency set to once per minute, the average value is taken after 10 collections to reduce noise interference.

[0084] For example, the collected humidity data for 10 times were: 65.2%, 64.8%, 65.5%, 66.0%, 64.9%, 65.3%, 65.7%, 64.7%, 65.1%, and 65.4%. The average value calculated by the algorithm was: (65.2+64.8+65.5+66.0+64.9+65.3+65.7+64.7+65.1+65.4) / 10 = 65.26%. The pressure difference inside the sealed chamber was detected by a pressure sensor, with a normal range set to -0.1 to 0.1 kPa. The collected data was 0.05 kPa, indicating a normal sealing condition. Next, the system determined whether the humidity had returned to the normal range (set to 60%-70%). By comparison, 65.26% was within the normal range, indicating that the humidity had recovered. The 0.05 kPa in the sealed state was also within the normal range, confirming the overall stability of the system. Updated environmental parameters are automatically recorded in the database in JSON format, including a timestamp (e.g., 2025-07-15 01:46:00), humidity (65.26%), and sealing pressure difference (0.05 kPa). To ensure data reliability, the system employs an anomaly detection algorithm. If the humidity data deviates from the standard deviation by more than two times (calculated standard deviation is approximately 0.41%), re-collection is triggered. Analysis results show no anomalies in the current data; the record is directly stored in the database and pushed to the monitoring system via API, forming a closed-loop management system. If humidity or sealing conditions are abnormal, the system automatically triggers an alarm and coordinates with temperature control equipment to adjust environmental parameters, ensuring the integrity of the logical chain.

[0085] For the updated environmental parameter records, a regular monitoring mechanism is used to upload the data to the management and collaboration platform, generating long-term monitoring reports for subsequent analysis.

[0086] The system acquires the latest environmental parameter data through a scheduled task system. Data is extracted from multiple monitoring points using a preset collection frequency to obtain a preliminary environmental parameter dataset. Based on this dataset, data cleaning tools remove outliers and missing values. If missing data exceeds a preset threshold, a supplementary data acquisition mechanism is triggered to retrieve supplementary data from backup data sources, ensuring a complete cleaned dataset. For the cleaned dataset, data standardization methods unify data format and units. Data from different sources is integrated using preset mapping rules to obtain a standardized set of environmental parameters. Key indicators are extracted from this standardized set, and a support vector machine algorithm is used to predict their trends. If the prediction shows an abnormal trend, relevant data points are marked, and early warning information is generated to identify potential risk areas. Based on the marked data points and early warning information, the processed environmental parameter set is uploaded to the management collaboration platform via a data transmission protocol. Upload status feedback is obtained to confirm data integrity. For the data uploaded to the management collaboration platform, a scheduled task triggers the data integration and analysis process. A structured long-term monitoring report is generated using a preset analysis template to obtain the analysis results. Extract key conclusions from the generated long-term monitoring reports, present the analysis results in chart form using data visualization tools, and store them in the management collaboration platform for subsequent querying and analysis.

[0087] Specifically, environmental parameters (such as temperature, humidity, and PM2.5 concentration) are collected periodically through a sensor network, with data acquired once per hour. For example, a temperature of 25.5℃, humidity of 60%, and PM2.5 concentration of 45 μg / m³ are collected. 3 The data is transmitted to edge computing nodes via the MQTT protocol in JSON format ({"temp":25.5,"humidity":60,"pm2.5":45}) for preprocessing. A sliding window algorithm (window size of 24 hours) is used to calculate the daily average and standard deviation. For example, if the daily average PM2.5 concentration is 40 μg / m³... 3 The standard deviation is 5 μg / m 3 To detect outliers (values ​​exceeding the mean ± 2 standard deviations), preprocessed data is uploaded to the management and collaboration platform via a RESTful API. The platform uses a PostgreSQL database for storage, with table structures including timestamps, parameter types, and numeric fields. Data is uploaded every 6 hours to ensure bandwidth usage remains below 100KB / s. The platform uses a Python script to call the Pandas library to generate long-term monitoring reports. These reports include time-series analysis (e.g., a 30-day moving average, calculated as SMA = ΣX_i / 30, where X_i is the daily average) and K-means clustering (k = 3) to classify data into high, medium, and low pollution levels, for example, PM2.5 > 50 μg / m³. 3High pollution level, 25-50 μg / m³ 3 Medium pollution level, <25 μg / m³ 3 The pollution level was low; clustering results showed 5 days of high pollution in the past 30 days. The report was generated in PDF format and included a line graph (X-axis for time, Y-axis for PM2.5 concentration) and statistical tables (e.g., average 40 μg / m³). 3 Variance 25 μg / m 32 The data is automatically pushed to the analysis module via the platform for subsequent trend prediction. The prediction model uses ARIMA (p=1, d=1, q=1) to predict the PM2.5 concentration for the next 7 days based on historical 30-day data, with the mean square error controlled within 10 μg / m³. 32 Within this range. The above process is implemented through automated scripts, with the following logical chain: data collection → preprocessing → uploading → storage → analysis → report generation → push, ensuring efficient data flow without human intervention.

[0088] In the description of this application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0089] Those skilled in the art should understand that the above embodiments are merely for illustrative purposes and are not intended to limit the scope of this application. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of this application.

Claims

1. A pipeline integration and layout module for a closed ring area, characterized in that: The module includes: The mounting column is fixed to the ground at its bottom. A cable tray for laying cables, the cable tray being detachably mounted on the mounting post, the top of the cable tray having an open structure with a cover plate for closing or opening the opening at the top of the cable tray; A tripod is installed near the bottom of the mounting column. The middle part of the tripod is used to support the water pipe, and the tripod is also used to lower the center of gravity of the module. The modules can be deployed around the construction site to form a modular enclosure surrounding the site, wherein the cable tray is located near the inside of the modular enclosure, the cables on the cable tray are the main network, and the cables connecting to electrical appliances from the cable tray are the branch lines. The module further includes: Electrical connection points are used to relocate municipal cables into the central control room. Central control room, where cables are moved into the cable trays; A water connection point is used to move municipal water pipes into the tripod.

2. The pipeline integration and layout module for a closed ring area according to claim 1, characterized in that: in, The tripod is a right-angled tripod, which has a first right-angled side, a second right-angled side, and a hypotenuse, wherein the length of the first right-angled side is greater than the length of the second right-angled side; The first end of the first right-angled side is fixed to the ground, the first end of the second right-angled side is fixed to the mounting post, and the other end is fixed to the second end of the first right-angled side; the first end of the hypotenuse is fixed to the mounting post, and the second end is fixed to the first end of the first right-angled side. So that the hypotenuse of the right-angle tripod faces the mounting post.

3. The pipeline integration and layout module for a closed ring area according to claim 1, characterized in that: The mounting post has several mounting holes equidistantly spaced along its height direction on its side, and the mounting holes form a row. One mounting post has two rows of mounting holes. The mounting holes include large holes and vertical slots; The cable tray is equipped with a mushroom-shaped pin, which can be inserted into the large hole and move downward along the vertical groove so that the end of the mushroom-shaped pin is locked inside the mounting post, so that the two sides of the cable tray are fixedly installed on the two mounting posts.

4. The pipeline integration and layout module for a closed ring area according to claim 3, characterized in that: The further module includes a fire hydrant, on which the mushroom-shaped pin is installed, and the fire hydrant can be installed on the mounting post via the mushroom-shaped pin. The fire hydrant's fire hose can be installed using the tripod.

5. A maintenance method, characterized in that: The maintenance method is used to maintain the pipeline integrated layout module for a closed ring area as described in any one of claims 1-4; the method includes: A humidity sensor is installed in the cable tray to collect humidity data and sealing status information inside the cable tray and generate an initial environmental parameter record. Based on the comparison between the humidity data in the initial environmental parameter record and the preset threshold range, the acquisition frequency of the sensor device is adjusted to obtain dynamically adjusted humidity data; By using time series analysis algorithms, the changing trend of the dynamically adjusted humidity data is analyzed to identify humidity anomalies. Based on the sealing status information and the humidity anomaly points, a decision tree algorithm is used to determine the sealing integrity inside the modular enclosure frame and generate a sealing status assessment result. The abnormal state features in the sealing state assessment results are classified using a clustering analysis algorithm to obtain the abnormal state classification results. Based on the abnormal state classification results, an environmental parameter adjustment instruction is generated and sent to the control unit of the modular enclosure frame to adjust the environmental parameters inside the modular enclosure frame and generate an optimized environmental parameter record. The optimized environmental parameter records are used to update the initial environmental parameter records, which are then stored in the data recording system to generate the final environmental parameter dataset.

6. The maintenance method according to claim 5, characterized in that: The step of adjusting the sampling frequency of the sensor device based on the comparison between the humidity data recorded in the initial environmental parameter record and a preset threshold range, and obtaining dynamically adjusted humidity data, includes: Humidity data is extracted from the initial environmental parameter records, and the data processing module is used to classify and organize the data to generate a classified humidity dataset. For each group of data in the classified humidity dataset, compare it with a preset threshold, mark the humidity data that exceeds the preset threshold as potential abnormal data, and generate a set of potential abnormal data; The support vector machine algorithm is used to analyze the potential abnormal data set, and combined with the historical fluctuation of environmental parameters, the true humidity anomaly is determined and a humidity anomaly identifier is generated. Based on the humidity anomaly indicator, the sampling frequency of the sensor device is adjusted to obtain dynamically adjusted humidity data.

7. The maintenance method according to claim 6, characterized in that: The process employs a support vector machine algorithm to analyze the potential anomaly dataset, combining this with historical fluctuations in environmental parameters to determine the actual humidity anomalies and generate humidity anomaly identifiers, including: By extracting the corresponding humidity data and environmental parameters from the potential abnormal data set, and using time series analysis methods, the persistence and changing trend of the humidity data are detected to generate abnormal change characteristics. Based on the abnormal change characteristics and combined with the historical records of the data processing module, the frequency and distribution patterns of the abnormal situation are analyzed to determine whether the abnormality is periodic, and the final humidity anomaly analysis results are generated. Based on the final humidity anomaly analysis results, corresponding anomaly classification labels are generated and stored in the data processing module.

8. The maintenance method according to claim 7, characterized in that: Based on the sealing status information and the humidity anomaly points, a decision tree algorithm is used to determine the sealing integrity inside the modular enclosure frame and generate a sealing status assessment result, including: The abnormal information corresponding to the humidity anomaly point is sent to the management collaboration platform through the data transmission channel to obtain the location information of the modular enclosure frame; Based on the location information, a decision tree algorithm is used to classify the severity of the abnormal information and generate anomaly level classification results. Based on the anomaly level classification results, a positioning calibration command is sent to obtain accurate position data of the modular enclosure frame.

9. The maintenance method according to claim 8, characterized in that: The process of updating the initial environmental parameter record using the optimized environmental parameter record, storing it in the data recording system, and generating the final environmental parameter dataset includes: The sealing status data and external environmental conditions of the modular enclosure frame are acquired by sensors, and a data preprocessing method is used to generate a processed sealing status dataset and environmental condition dataset. The support vector machine algorithm is used to classify the processed sealed state dataset and the environmental condition dataset to generate an adaptability evaluation result of the modular enclosure frame under the current environmental conditions. Based on the adaptive assessment results, the performance parameters of the modular enclosure frame are adjusted, new sealing status data is generated, and the initial environmental parameter records are updated.

10. The maintenance method according to claim 9, characterized in that: The step of adjusting the performance parameters of the modular enclosure frame based on the adaptability assessment results, generating new sealing status data, and updating the initial environmental parameter records includes: Based on the adaptive assessment results, a cable protection command is generated and transmitted to the modular enclosure frame; The execution status of the cable protection command is obtained through the real-time feedback channel, the protection settings of the modular enclosure frame are adjusted, and the adjusted protection status data is generated. Based on the adjusted protection status data, the initial environmental parameter record is updated and stored in the data recording system.

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