A vacuum degree monitoring system and method for vacuum preloading soil

Abstract

The present invention relates to the field of vacuum monitoring technology, and in particular to a vacuum preloading soil vacuum degree monitoring system and method, the monitoring system comprising: a plurality of acquisition modules, a plurality of vacuum degree sensors, a first comparison module generation module, a second comparison module, a marking module, a division module, a third comparison module and an adjustment module; obtaining the preloading data of each monitoring point in the target area, and determining whether the preloading conditions of each monitoring point meet the preset standards; analyzing the preloading data of each monitoring point, and classifying and marking each monitoring point according to the analysis results; dividing the target area into regions according to the classification and marking results, and determining the actual operation mode of each vacuum pump according to the regional division results. The present invention can effectively identify problems existing in the preloading process, timely adjust the operation mode of the vacuum pump, ensure the optimization of the preloading effect, improve the utilization rate of equipment such as vacuum pumps, and reduce resource waste.

Description

Technical Field

[0001] The invention relates to the technical field of vacuum monitoring, and in particular to a vacuum degree monitoring system and method for vacuum preloading soil. Background Art

[0002] At present, when vacuum preloading is performed on the soil in the target area, it is impossible to adapt to the preloading needs of different areas and realize refined management of the target area; it is also impossible to effectively identify the problems existing in the preloading process and adjust the operation mode of the vacuum pump in time to ensure the optimization of the preloading effect, resulting in low utilization rate of equipment such as vacuum pumps and waste of resources.

[0003] Chinese patent application publication number CN108918023A discloses a vacuum degree monitoring device for vacuum preloading soil, comprising a vacuum degree reading fixed end, a vacuum pressure tube, a vacuum probe and a geotextile: one end of the vacuum pressure tube is connected to the vacuum degree reading fixed end fixed on the vacuum preloading sealing membrane, and the other end is connected to the vacuum probe buried in the vacuum soil or under the vacuum preloading sealing membrane; the vacuum probe comprises a hard pressure tube, a conical top cover and a fixing rope, one end of the vacuum pressure tube extends into the hard pressure tube and is fixed in the hard pressure tube by the fixing rope; pressure openings are respectively provided on the section of the vacuum pressure tube extending into the hard pressure tube and the middle section of the hard pressure tube, one end of the hard pressure tube is provided with a conical top cover sealed on the hard pressure tube, the outer surface of the hard pressure tube is wrapped with a geotextile, and the geotextile is wrapped and tied and fixed to the vacuum pressure tube outside the other end of the hard pressure tube.

[0004] It can be seen that the current vacuum preloading soil vacuum degree monitoring system cannot ensure the optimization of the preloading effect. Summary of the invention

[0005] Therefore, the object of the present invention is to provide a vacuum degree monitoring system and method for vacuum preloading soil, so as to overcome the problem that the current vacuum degree monitoring system for vacuum preloading soil cannot ensure the optimization of preloading effect.

[0006] To achieve the above object, the present invention provides a vacuum degree monitoring system for vacuum preloading soil, comprising:

[0007] Several collection modules for collecting the actual water discharged by each drainage board;

[0008] Several vacuum sensors for monitoring the actual vacuum level in each drain plate;

[0009] A first comparison module, which is used to compare the single actual moisture content with the actual vacuum degree to determine the actual single-point pre-pressure condition of each monitoring point;

[0010] A generation module, which is used to generate a spatial humidity distribution curve of a target area according to each individual actual moisture;

[0011] A second comparison module, which is used to analyze according to the spatial humidity distribution curve to determine the pre-pressure abnormality level of the target area;

[0012] A marking module, which is used to classify and mark each monitoring point according to a single actual moisture change combined with the preload abnormality level;

[0013] A division module is used to divide the area according to the marking type of each monitoring point and determine the actual area of ​​each type of area;

[0014] A third comparison module is used to analyze the actual area to determine the actual preload evaluation value in each type of area;

[0015] A regulating module, which is used to determine the actual operation mode of each vacuum pump according to the actual pre-pressure evaluation value;

[0016] Among them, the target area is monitored in real time to determine the pre-pressure conditions of each monitoring point in the target area; a collective analysis is performed based on the actual parameters monitored, and the marking type of the target area is determined through the collective analysis results; and the target area is divided into regions based on the determined classification marking results, and the actual operation mode of each vacuum pump is determined through the regional division results.

[0017] Furthermore, each drain plate is recorded as a different monitoring point, and the acquisition module and the vacuum sensor are provided for any monitoring point;

[0018] The first comparison module determines, based on the first ratio and the preset first evaluation value, that the actual single-point preloading condition of the monitoring point meets the preset standard;

[0019] The first ratio is the ratio between the single actual moisture monitored by the acquisition module at the monitoring point and the actual vacuum degree monitored by the vacuum sensor.

[0020] Further, if the first ratio is less than or equal to the first evaluation value, the first comparison module determines that the actual single-point preloading condition of the monitoring point meets the preset standard;

[0021] If the first ratio is greater than the first evaluation value, the first comparison module determines that the actual single-point preload condition of the monitoring point does not meet the preset standard, and determines that the monitoring point meets the single alarm condition.

[0022] Furthermore, the second comparison module determines the number of items that meet the single alarm condition at each monitoring point according to the spatial humidity distribution curve; and can determine the preload abnormality level according to the number of items that meet the single alarm condition;

[0023] If the number of items that meet the single alarm condition in each monitoring point is 0, the second comparison module determines that the actual overall pre-pressure condition meets the preset standard.

[0024] Further, if the number of items that meet the single alarm condition in each monitoring point is not 0 and is unique, the second comparison module determines that the actual overall preloading condition does not meet the preset standard, the preloading abnormality level is level one, and sends a first maintenance signal to perform maintenance on the vacuum preloading equipment;

[0025] If the number of items that meet the single alarm condition in each monitoring point is not 0 and is not unique, the second comparison module determines that the actual overall preloading condition does not meet the preset standard, the preloading abnormality level is level 2, and a first alarm signal is issued to shut down and re-arrange the vacuum preloading equipment.

[0026] Furthermore, a collection cycle is provided in the marking module, and a single actual moisture change amount in any collection cycle is obtained according to the collection cycle;

[0027] The marking module calculates a second difference absolute value based on the single actual moisture change and the preset single standard moisture change; when the actual overall pre-compression condition of the target area meets the preset standard or the pre-compression abnormality level is level one; determines the marking type based on the second difference absolute value combined with a preset second evaluation value.

[0028] Further, if the absolute value of the second difference is less than or equal to the second evaluation value, the marking module marks the monitoring point as a normal type;

[0029] The second absolute value of the difference is the absolute value of the difference between the single actual moisture change and the single standard moisture change.

[0030] Further, if the absolute value of the second difference is greater than the second evaluation value and the single actual moisture change is less than the single standard moisture change, the marking module marks the monitoring point as a dry type;

[0031] If the absolute value of the second difference is greater than the second evaluation value and the single actual moisture change is greater than the single standard moisture change, the marking module marks the monitoring point as a wet type.

[0032] Furthermore, the third comparison module integrates the marker types of each monitoring point; calculates the actual pre-pressure evaluation value according to the integration result; and determines the actual operation mode of each vacuum pump according to the actual pre-pressure evaluation value;

[0033] If the actual pre-pressure evaluation value is greater than or equal to the preset standard pre-pressure evaluation value, it is determined that the actual operation mode of each vacuum pump is: distributed operation mode;

[0034] If the actual pre-pressure evaluation value is less than the standard pre-pressure evaluation value, it is determined that the actual operation mode of each vacuum pump is a fully open operation mode.

[0035] Another aspect of the present invention further provides a method for monitoring the vacuum degree of a vacuum preloaded soil body, comprising:

[0036] Step S1, obtaining pre-compression data of each monitoring point in the target area, and determining whether the pre-compression condition of each monitoring point meets a preset standard;

[0037] Step S2, analyzing the preloading data of each monitoring point, and classifying and marking each monitoring point according to the analysis results;

[0038] Step S3, dividing the target area into regions according to the classification and labeling results, and determining the actual operation mode of each vacuum pump according to the region division results.

[0039] Compared with the prior art, the beneficial effects of the present invention are that, through a number of acquisition modules and vacuum sensors, the actual moisture and vacuum degree of each monitoring point in the target area are monitored in real time to ensure accurate data; the single actual moisture and actual vacuum degree can be compared instantly to determine the actual single-point preload situation of each monitoring point, and provide real-time preload status feedback; a spatial humidity distribution curve of the target area is generated to provide a detailed humidity change map; analysis is performed based on the humidity distribution curve to determine the abnormal preload level, which helps to timely discover and deal with preload problems; monitoring points are classified and marked according to the actual moisture change and the abnormal preload level, providing refined preload status classification; based on the marking type of the monitoring point Carry out regional division and determine the actual area of ​​each type of area to ensure the accuracy of regional division; analyze the actual area, determine the actual preloading evaluation value of each type of area, and provide a comprehensive evaluation of the regional preloading situation; determine the actual operation mode of each vacuum pump according to the actual preloading evaluation value, optimize the operation of the vacuum pump, and improve the efficiency and effect of the system; through collective analysis and regional division, it can adapt to the preloading needs of different areas and realize refined management of the target area; through precise monitoring and analysis, it can effectively identify problems in the preloading process, adjust the operation mode of the vacuum pump in time, and ensure the optimization of the preloading effect; optimize resource allocation and scheduling, improve the utilization rate of vacuum pumps and other equipment, and reduce resource waste.

[0040] In particular, by setting up a separate acquisition module and vacuum sensor at each monitoring point, the actual moisture and actual vacuum degree of each point can be monitored independently; through the coordinated work of the acquisition module and the vacuum sensor, the specific data of each point can be obtained to improve the accuracy of monitoring; through a detailed analysis of the pre-pressure condition of each monitoring point; it is possible to accurately judge whether the actual single-point pre-pressure condition of each monitoring point meets the preset standard, providing a refined analysis tool; real-time judgment of whether the pre-pressure condition of the monitoring point meets the standard; timely judgment of whether the point meets the single alarm condition, providing real-time early warning information; through real-time monitoring and instant analysis, it is possible to quickly respond to abnormal conditions in the pre-pressure process and adjust relevant measures in time; the rapid response capability helps Prevent the problem from further deteriorating and ensure the stability and efficiency of the preloading process; through automated monitoring and analysis, reduce the need for manual intervention and improve management efficiency; the automated early warning mechanism can promptly remind operators or automatically trigger subsequent processing measures, improving the intelligence level of the entire system; the sophisticated monitoring and analysis mechanism ensures that the preloading conditions of each point can meet the preset standards, improving the quality of the preloading process as a whole; by promptly discovering and handling points that do not meet the standards, avoid the overall preloading effect due to local problems and improve the preloading quality; through precise preloading monitoring and adjustment, avoid waste of resources and improve resource utilization efficiency; timely adjustment of points that do not meet the standards reduces the number of repeated preloading and saves time and cost.

[0041] In particular, the spatial humidity distribution curve is generated through the actual moisture data of each monitoring point, providing a comprehensive map of the humidity in the target area; the comprehensive analysis method helps to understand the humidity conditions of the entire target area more accurately and improve the accuracy of overall monitoring; by analyzing the spatial humidity distribution curve, the number of items that meet the single alarm condition in each monitoring point is determined, and the preloading abnormality is effectively detected; it is ensured that even if an abnormality occurs at a single monitoring point, it can be discovered and handled in time to avoid local problems affecting the overall preloading effect; a graded early warning mechanism is set up according to the number of items that meet the single alarm condition; the graded early warning mechanism can take different response measures according to the degree of abnormality, improve the system's responsiveness and management efficiency; by sending out maintenance signals and alarm signals in real time, the vacuum preloading equipment can be maintained and Adjustment can prevent the problem from further expanding; timely maintenance and adjustment mechanism can help ensure the stability and continuity of the preloading process and improve the overall operating efficiency; through accurate abnormality detection and graded processing, it can reasonably allocate maintenance and adjustment resources, avoid unnecessary downtime and duplication of work, and improve resource utilization efficiency; effective resource allocation can help reduce operating costs and improve economic benefits; through comprehensive analysis and graded early warning, it can ensure that the preloading effect of each monitoring point and the entire target area meets the preset standards and improve the preloading quality; timely maintenance and adjustment measures can quickly solve problems and maintain the efficient operation of the preloading process; through automatic generation of humidity distribution curves and automatic detection of preloading anomalies, highly intelligent monitoring and control are achieved; the level of intelligence reduces manual intervention and improves automated management capabilities.

[0042] In particular, the actual moisture change of a single unit is obtained in real time according to a predetermined period to ensure the continuity of dynamic monitoring and the timeliness of data; through periodic data collection, the trend of moisture change in the target area can be reflected in real time, improving the accuracy of analysis; the second difference absolute value is calculated based on the actual moisture change of a single unit and the preset standard moisture change of a single unit to ensure accurate calculation of moisture change, thereby marking the area where the monitoring point is located as normal type, dry type or wet type, providing a refined classification method; accurate classification helps to take corresponding adjustment measures for different types of areas and improve the scientific nature of preloading management; through dynamic classification of monitoring points, abnormal areas can be identified and handled in a timely manner to ensure the effectiveness and stability of the overall preloading process; refined management based on real-time data Management helps to improve the preloading effect and avoid the overall preloading quality being affected by problems at individual points; automatic data collection, calculation and classification reduce the need for manual intervention and improve management efficiency; the automated classification and marking mechanism enables rapid response to various situations that arise during the preloading process, improving overall management efficiency and effectiveness; through precise classification and adjustment measures, it can optimize the allocation and utilization of resources and avoid waste of resources; taking corresponding adjustment measures for different types of areas reduces unnecessary operations, improves resource utilization efficiency and saves costs; the intelligent analysis and classification mechanism of the marking module improves the intelligence level of the system and realizes refined management of the preloading process; it helps to maintain efficient operation in complex environments and improves overall reliability and intelligence.

[0043] In particular, the target area is divided into normal area, dry area and wet area by marking type, and the actual area of ​​each type of area is calculated. The accurate area division method is helpful to understand the humidity conditions of different areas and provides detailed basic data support; the monitoring points of normal type, dry type and wet type are renumbered, which is helpful for the systematization and refinement of data management; the renumbering makes the subsequent data integration and analysis more orderly and improves the operating efficiency of the system; the data is integrated according to the marking type of each monitoring point, and the actual preloading evaluation value is calculated to reflect the operation status of the entire preloading system; the comprehensive evaluation mechanism can reflect the preloading effect more comprehensively and provide a scientific basis for decision-making; the flexible operation mode can adjust the equipment operation according to the actual situation, which improves the operation efficiency and energy saving effect of the system; through flexible operation mode and precise Accurate regional division can optimize resource utilization and avoid unnecessary waste of resources; different operating strategies can be adopted according to the actual conditions of different regions, which will help improve resource utilization efficiency and reduce operating costs; through comprehensive evaluation mechanism and flexible operation mode, the operating status of preloading equipment can be adjusted in time to ensure that the preloading effect meets the preset standards; real-time monitoring and adjustment measures can help maintain the efficient operation of the preloading process and improve the overall preloading effect; through automated data collection, calculation and evaluation, intelligent management of the preloading process is realized; highly intelligent management capabilities reduce manual intervention and improve the automation level and management efficiency of the system; through precise division of each area and real-time monitoring, abnormal situations can be discovered in time; the ability to prevent and respond to abnormalities helps to avoid possible problems in the preloading process and ensure the stable operation of the system.

[0044] In particular, by obtaining the pre-stressing data of each monitoring point in the target area, the system is ensured to have a comprehensive understanding of the pre-stressing situation; the comprehensive collection of pre-stressing data provides sufficient information support for subsequent analysis and decision-making; by determining whether the pre-stressing situation of each monitoring point meets the preset standards, the current pre-stressing status can be quickly evaluated; the standardized evaluation method helps to identify areas that do not meet the pre-stressing requirements, facilitating timely adjustments; a detailed analysis is carried out based on the pre-stressing data of each monitoring point, and each monitoring point is classified and marked according to the analysis results; the precise analysis and classification methods can accurately identify the specific conditions of each point, which is helpful for subsequent optimization and adjustment; the target area is divided into regions according to the classification and marking results, so that the conditions of different types of areas are clear at a glance; accurate regional division helps to manage the pre-stressing process more effectively and take corresponding measures for different areas; according to the regional division results, the actual operation mode of each vacuum pump can be determined to ensure resource Reasonable utilization; by optimizing the operation mode, the operating efficiency of the system can be improved, energy consumption can be reduced, and energy-saving effects can be achieved; the operation mode of the vacuum pump can be flexibly adjusted according to real-time data and analysis results to ensure the stability and reliability of the preloading effect; the flexible response and adjustment capabilities can respond to various changes in the preloading process in a timely manner to ensure the normal operation of the system; automated data acquisition, analysis, classification and area division reduce manual intervention and improve management efficiency; automated management processes enable the system to respond and adjust quickly, improving the overall management effect; through precise data analysis and optimized operation mode, the preloading effect can be effectively improved to ensure that the preloading process meets the preset standards; the enhanced preloading effect helps to improve the quality of products or projects and meet expected requirements; by optimizing resource allocation and utilization, resource waste can be avoided and costs can be saved; adopting different operation strategies for different types of areas helps to maximize resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a structural schematic diagram of a vacuum degree monitoring system for vacuum preloading soil according to an embodiment of the present invention;

[0046] Figure 2 A logic diagram for judging abnormal preloading level in a vacuum preloading soil vacuum degree monitoring system according to an embodiment of the present invention;

[0047] Figure 3 A logic diagram for selecting an actual operation mode in a vacuum preloading soil vacuum degree monitoring system according to an embodiment of the present invention;

[0048] Figure 4 It is a flow chart of a method for monitoring vacuum degree of vacuum preloading soil body according to an embodiment of the present invention;

[0049] The figure includes: a collection module 1, a vacuum sensor 2, a first comparison module 3, a generation module 4, a second comparison module 5, a marking module 6, a division module 7, a third comparison module 8 and an adjustment module 9. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0052] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0053] See also Figure 1-Figure 4 As shown, Figure 1 It is a structural schematic diagram of a vacuum degree monitoring system for vacuum preloading soil according to an embodiment of the present invention; Figure 2 A logic diagram for judging abnormal preloading level in a vacuum preloading soil vacuum degree monitoring system according to an embodiment of the present invention; Figure 3 A logic diagram for selecting an actual operation mode in a vacuum preloading soil vacuum degree monitoring system according to an embodiment of the present invention; Figure 4 The present invention is a flowchart of a method for monitoring the vacuum degree of a vacuum preloaded soil body according to an embodiment of the present invention.

[0054] In one aspect, the present invention provides a vacuum degree monitoring system for vacuum preloading soil, comprising:

[0055] Several acquisition modules 1, several vacuum sensors 2, a first comparison module 3, a generation module 4, a second comparison module 5, a marking module 6, a division module 7, a third comparison module 8 and an adjustment module 9;

[0056] The monitoring system in this embodiment is used to monitor the target area in real time to determine the pre-pressure conditions of each monitoring point in the target area; perform a set analysis based on the actual parameters monitored, and determine the marking type of the target area through the set analysis results; and divide the target area into regions according to the determined classification marking results, and determine the actual operation mode of each vacuum pump through the region division results;

[0057] In the process of vacuum preloading the soil in this embodiment, a drainage assembly needs to be arranged in the target area; the drainage assembly includes a plurality of drainage boards, each of which is vertically inserted into the target area, and each of the drainage boards is recorded as a plurality of monitoring points;

[0058] Each collection module 1 is used to collect a single actual amount of water discharged by each drainage board;

[0059] Each vacuum sensor 2 is used to monitor the actual vacuum degree in each drain plate;

[0060] The first comparison module 3 is used to compare the single actual moisture content with the actual vacuum degree to determine the actual single point pre-pressure condition of each monitoring point;

[0061] The generating module 4 is used to generate a spatial humidity distribution curve of the target area according to each single actual moisture;

[0062] The second comparison module 5 is used to analyze the spatial humidity distribution curve to determine the pre-pressure abnormality level of the target area;

[0063] The marking module 6 is used to classify and mark each monitoring point according to a single actual moisture change combined with the preload abnormality level;

[0064] The division module 7 is used to divide the area according to the marking type of each monitoring point and determine the actual area of ​​each type of area;

[0065] The third comparison module 8 is used to analyze the actual area to determine the actual preload evaluation value in each type of area;

[0066] The regulating module 9 is used to determine the actual operation mode of each vacuum pump according to the actual pre-pressure evaluation value.

[0067] The embodiment of the present invention uses a number of acquisition modules and vacuum sensors to monitor the actual moisture and vacuum degree of each monitoring point in the target area in real time, ensuring the acquisition of accurate data; it can instantly compare the single actual moisture and actual vacuum degree, determine the actual single-point pre-pressure situation of each monitoring point, and provide real-time pre-pressure state feedback; generate a spatial humidity distribution curve of the target area, and provide a detailed humidity change map; analyze according to the humidity distribution curve to determine the pre-pressure abnormality level, which is helpful to timely discover and deal with pre-pressure problems; classify and mark the monitoring points according to the actual moisture change and the pre-pressure abnormality level, and provide refined pre-pressure state classification; divide the area according to the mark type of the monitoring point, And determine the actual area of ​​each type of area to ensure the accuracy of regional division; analyze the actual area, determine the actual preloading evaluation value of each type of area, and provide a comprehensive assessment of the regional preloading situation; determine the actual operation mode of each vacuum pump according to the actual preloading evaluation value, optimize the operation of the vacuum pump, and improve the efficiency and effect of the system; through collective analysis and regional division, it can adapt to the preloading needs of different areas and realize refined management of the target area; through precise monitoring and analysis, it can effectively identify problems in the preloading process, adjust the operation mode of the vacuum pump in time, and ensure the optimization of the preloading effect; optimize resource allocation and scheduling, improve the utilization rate of vacuum pumps and other equipment, and reduce resource waste.

[0068] Specifically, the drainage assembly in this embodiment includes a plurality of drainage boards, and each drainage board is recorded as a different monitoring point, including: a first monitoring point A1, a second monitoring point A2, ..., an nth monitoring point An;

[0069] For any monitoring point Ai, where i=1, 2, ..., n; the monitoring point is provided with an i-th acquisition module and an i-th vacuum sensor;

[0070] The i-th acquisition module is used to collect the i-th single actual moisture B1i at the monitoring point; the i-th vacuum sensor is used to monitor the i-th actual vacuum degree B2i at the monitoring point;

[0071] The first comparison module 3 calculates a first ratio S1 i according to the i-th single actual moisture content B1 i and the i-th actual vacuum degree B2i; S1 i=B1 i / B2i;

[0072] If S1 i≤S10, the first comparison module 3 determines that the actual single-point preloading condition of the monitoring point meets the preset standard;

[0073] If S1 i>S10, the first comparison module 3 determines that the actual single-point preload condition of the monitoring point does not meet the preset standard, and determines that the monitoring point meets the single alarm condition;

[0074] Wherein, S10 is a preset first evaluation value;

[0075] In this embodiment, the specific setting of the first evaluation value S10 is also affected by the permeability of the soil and the preloading time. In the early stage of preloading, the relationship between the actual vacuum degree and the single actual moisture content should satisfy that the higher the actual vacuum degree, the easier it is to extract the moisture in the soil, and the larger the value of the single actual moisture content. However, after reaching a certain vacuum degree, the growth rate of the amount of water obtained will slow down. This is because most of the moisture has been extracted. Therefore, the value of the first evaluation value S10 at this time will also change accordingly, and soil with good permeability can release moisture faster during the vacuum preloading process.

[0076] The embodiment of the present invention sets a separate acquisition module and vacuum sensor at each monitoring point to ensure that the actual moisture and actual vacuum degree of each point can be monitored independently; through the coordinated work of the acquisition module and the vacuum sensor, the specific data of each point can be obtained to improve the accuracy of monitoring; through the detailed analysis of the pre-pressure condition of each monitoring point, it is possible to accurately judge whether the actual single-point pre-pressure condition of each monitoring point meets the preset standard, and provide a refined analysis tool; in real time, it is possible to judge whether the pre-pressure condition of the monitoring point meets the standard; it is possible to promptly determine whether the point meets the single alarm condition and provide real-time early warning information; through real-time monitoring and instant analysis, it is possible to quickly respond to abnormal conditions in the pre-pressure process and adjust relevant measures in time; the rapid response capability helps It is used to prevent the problem from further deteriorating and ensure the stability and efficiency of the preloading process; through automated monitoring and analysis, the need for manual intervention is reduced and management efficiency is improved; the automated early warning mechanism can promptly remind operators or automatically trigger subsequent processing measures, thereby improving the intelligence level of the entire system; the sophisticated monitoring and analysis mechanism ensures that the preloading conditions of each point can meet the preset standards, thereby improving the quality of the preloading process as a whole; by promptly discovering and handling points that do not meet the standards, the overall preloading effect is avoided from being affected by local problems, thereby improving the quality of preloading; through precise preloading monitoring and adjustment, waste of resources is avoided and resource utilization efficiency is improved; timely adjustments are made to points that do not meet the standards, thereby reducing the number of repeated preloading and saving time and costs.

[0077] Specifically, the generating module 4 in this embodiment generates the spatial humidity distribution curve of the target area according to each single actual moisture obtained at each monitoring point;

[0078] The second comparison module 5 determines the number of items that meet the single alarm condition at each monitoring point according to the spatial humidity distribution curve;

[0079] If the number of items that meet the single alarm condition in each monitoring point is 0, the second comparison module 5 determines that the actual overall preload condition of the target area meets the preset standard;

[0080] If the number of items that meet the single alarm condition in each monitoring point is not 0 and is unique, the second comparison module 5 determines that the actual overall preloading condition of the target area does not meet the preset standard, the preloading abnormality level is level one, and sends a first maintenance signal to perform maintenance on the vacuum preloading equipment;

[0081] If the number of items that meet the single alarm condition in each monitoring point is not 0 and is not unique, the second comparison module 5 determines that the actual overall preloading condition of the target area does not meet the preset standard, the preloading abnormality level is level 2, and a first alarm signal is issued to shut down and re-deploy the vacuum preloading equipment.

[0082] The embodiment of the present invention generates a spatial humidity distribution curve through the actual moisture data of each monitoring point, and provides a comprehensive map of the humidity in the target area; the comprehensive analysis method helps to understand the humidity status of the entire target area more accurately, and improves the accuracy of overall monitoring; by analyzing the spatial humidity distribution curve, the number of items that meet the single alarm condition in each monitoring point is determined, and the preloading abnormality is effectively detected; it is ensured that even if an abnormality occurs at a single monitoring point, it can be discovered and handled in time to avoid local problems affecting the overall preloading effect; a graded early warning mechanism is set according to the number of items that meet the single alarm condition; the graded early warning mechanism can take different countermeasures according to the degree of abnormality, and improve the responsiveness and management efficiency of the system; by sending out maintenance signals and alarm signals in real time, the vacuum preloading equipment can be maintained in time Maintenance and adjustment can prevent the problem from further expanding; timely maintenance and adjustment mechanism helps to ensure the stability and continuity of the preloading process and improve the overall operating efficiency; through accurate abnormality detection and graded processing, it can reasonably allocate maintenance and adjustment resources, avoid unnecessary downtime and duplication of work, and improve resource utilization efficiency; effective resource allocation helps to reduce operating costs and improve economic benefits; through comprehensive analysis and graded early warning, it can ensure that the preloading effect of each monitoring point and the entire target area meets the preset standards and improve the preloading quality; timely maintenance and adjustment measures can quickly solve problems and maintain the efficient operation of the preloading process; through automatic generation of humidity distribution curves and automatic detection of preloading anomalies, highly intelligent monitoring and control are achieved; the level of intelligence reduces manual intervention and improves automated management capabilities.

[0083] Specifically, in this embodiment, a collection cycle T is set in the marking module 6; a single actual moisture change D1 in any collection cycle is obtained according to the collection cycle T;

[0084] The marking module 6 calculates a second difference absolute value S2 according to the single actual moisture change D1 and the preset single standard moisture change D10; S2 = |D1-D10|;

[0085] When the actual overall preload condition of the target area meets the preset standard or the preload abnormality level is level one;

[0086] If S2≤S20, the marking module 6 marks the monitoring point as a normal type;

[0087] If S2>S20 and D1<D10, the marking module 6 marks the monitoring point as a dry type;

[0088] If S2>S20 and D1>D10, the marking module 6 marks the monitoring point as a wet type;

[0089] Wherein, S20 is a preset second evaluation value;

[0090] In this embodiment, the collection period T is set to 1H; the specific settings of the collection period T and the first evaluation value S10 are also affected by the permeability of the soil and the preloading time. In the early stage of preloading, the relationship between the actual vacuum degree and the single actual moisture content should satisfy that the higher the actual vacuum degree, the easier it is to extract the moisture in the soil, and the larger the value of the single actual moisture content. However, after reaching a certain vacuum degree, the growth rate of the amount of water obtained will slow down. This is because most of the moisture has been extracted. Therefore, the collection period and the value of the second evaluation value S20 at this time will also change accordingly, and soil with good permeability can release moisture faster during the vacuum preloading process.

[0091] The embodiment of the present invention obtains a single actual moisture change in real time according to a predetermined period, ensuring the continuity of dynamic monitoring and the timeliness of data; through periodic data collection, it can reflect the trend of moisture changes in the target area in real time, and improve the accuracy of analysis; the second difference absolute value is calculated according to the single actual moisture change and the preset single standard moisture change to ensure accurate calculation of moisture changes, so as to mark the area where the monitoring point is located as normal type, dry type or wet type, providing a refined classification method; accurate classification is helpful to take corresponding adjustment measures for different types of areas, and improve the scientific nature of preloading management; through dynamic classification of monitoring points, abnormal areas can be identified and processed in time to ensure the effectiveness and stability of the overall preloading process; based on the refined real-time data Digital management helps to improve the preloading effect and avoid the overall preloading quality being affected by problems at individual points; automatic data collection, calculation and classification reduce the need for manual intervention and improve management efficiency; the automated classification and marking mechanism enables rapid response to various situations that arise during the preloading process, improving overall management efficiency and effectiveness; through precise classification and adjustment measures, it can optimize the allocation and utilization of resources and avoid waste of resources; taking corresponding adjustment measures for different types of areas reduces unnecessary operations, improves resource utilization efficiency and saves costs; the intelligent analysis and classification mechanism of the marking module improves the intelligence level of the system and realizes refined management of the preloading process; it helps to maintain efficient operation in complex environments and improves overall reliability and intelligence.

[0092] Specifically, the division module 7 in this embodiment divides the target area into regions according to the marking type; obtains the actual area of ​​each type of region, including: the actual normal area G1, the actual dry area G2 and the actual wet area G3;

[0093] The third comparison module 8 performs integration according to the marking type of each monitoring point, including:

[0094] 1) Renumber the number of normal type monitoring points, record them as the first normal point H11, the second normal point H12...the mth normal point H1m,

[0095] 2) Renumber the number of monitoring points of the drying type, recording them as the first drying point H21, the second drying point H22...the pth drying point H2k,

[0096] 2) Renumber the number of monitoring points of the wet type, recording them as the first wet point H31, the second wet point H32...the qth wet point H3q,

[0097] Calculate the actual preload evaluation value R and set

[0098]

[0099] Wherein, H2y is the single actual moisture change of the yth dry point, Ey is the first calculation compensation parameter of the single actual moisture change of the yth dry point to the actual pre-pressure evaluation value R; H3x is the single actual moisture change of the xth wet point, Dx is the second calculation compensation parameter of the single actual moisture change of the xth wet point to the actual pre-pressure evaluation value R; f1 is the first calculation adjustment parameter of the actual dry area G2 to the actual pre-pressure evaluation value R; f2 is the second calculation adjustment parameter of the actual wet area G3 to the actual pre-pressure evaluation value R;

[0100] If R≥R0, it is determined that the actual operation mode of each vacuum pump is: distributed operation mode;

[0101] If R<R0, it is determined that the actual operation mode of each vacuum pump is: fully open operation mode;

[0102] The distributed operation mode is to turn on the vacuum pumps corresponding to the normal type and the wet type, and turn off the vacuum pump corresponding to the dry type; the fully open operation mode is to turn on all vacuum pumps; R0 is the preset standard pre-pressure evaluation value;

[0103] In this embodiment, when preloading the soil, the setting of the preset standard preloading evaluation value is related to multiple factors; these factors mainly include the physical and mechanical properties of the soil, engineering requirements, construction conditions and environmental impacts; among them, the foundation bearing capacity required by the project is the main basis for setting the preloading evaluation value; after preloading, the soil needs to reach a specific bearing capacity standard to meet the safety and stability requirements of the building or structure; the allowable settlement range needs to be considered to ensure the long-term stability of the structure.

[0104] The embodiment of the present invention divides the target area into normal area, dry area and wet area by marking type, and calculates the actual area of ​​each type of area. The accurate area division method is helpful to understand the humidity conditions of different areas and provides detailed basic data support; the monitoring points of normal type, dry type and wet type are renumbered, which is helpful to systematize the data and refine the management; the renumbering makes the subsequent data integration and analysis more orderly and improves the operation efficiency of the system; the data is integrated according to the marking type of each monitoring point, and the actual preloading evaluation value is calculated to reflect the operation status of the entire preloading system; the comprehensive evaluation mechanism can reflect the preloading effect more comprehensively and provide a scientific basis for decision-making; the flexible operation mode can adjust the equipment operation according to the actual situation, improve the operation efficiency and energy saving effect of the system; through the flexible operation mode and Accurate regional division can optimize resource utilization and avoid unnecessary waste of resources; adopting different operating strategies according to the actual conditions of different regions can help improve resource utilization efficiency and reduce operating costs; through a comprehensive evaluation mechanism and flexible operating methods, the operating status of the preloading equipment can be adjusted in time to ensure that the preloading effect meets the preset standards; real-time monitoring and adjustment measures help maintain the efficient operation of the preloading process and improve the overall preloading effect; through automated data collection, calculation and evaluation, intelligent management of the preloading process is achieved; highly intelligent management capabilities reduce manual intervention and improve the system's automation level and management efficiency; through precise division of each area and real-time monitoring, abnormal situations can be discovered in a timely manner; the ability to prevent and respond to abnormalities helps avoid possible problems during the preloading process and ensure the stable operation of the system.

[0105] Another aspect of the present invention provides a method for monitoring the vacuum degree of a vacuum preloaded soil body, comprising:

[0106] Step S1, obtaining pre-compression data of each monitoring point in the target area, and determining whether the pre-compression condition of each monitoring point meets a preset standard;

[0107] Step S2, analyzing the preloading data of each monitoring point, and classifying and marking each monitoring point according to the analysis results;

[0108] Step S3, dividing the target area into regions according to the classification and labeling results, and determining the actual operation mode of each vacuum pump according to the region division results.

[0109] The embodiment of the present invention ensures that the system has a comprehensive understanding of the pre-pressure situation by acquiring the pre-pressure data of each monitoring point in the target area; the comprehensive collection of pre-pressure data provides sufficient information support for subsequent analysis and decision-making; by determining whether the pre-pressure situation of each monitoring point meets the preset standard, the current pre-pressure state can be quickly evaluated; the standardized evaluation method helps to identify areas that do not meet the pre-pressure requirements, which is convenient for timely adjustment; a detailed analysis is performed based on the pre-pressure data of each monitoring point, and each monitoring point is classified and marked according to the analysis results; the precise analysis and classification method can accurately identify the specific situation of each point, which is helpful for subsequent optimization and adjustment; the target area is divided into regions according to the classification and marking results, so that the situations of different types of regions are clear at a glance; accurate regional division helps to manage the pre-pressure process more effectively and take corresponding measures for different regions; according to the regional division results, the actual operation mode of each vacuum pump can be determined to ensure the quality of the resources. Reasonable use of resources; by optimizing the operation mode, the operating efficiency of the system can be improved, energy consumption can be reduced, and energy-saving effects can be achieved; the operation mode of the vacuum pump can be flexibly adjusted according to real-time data and analysis results to ensure the stability and reliability of the preloading effect; the flexible response and adjustment capabilities can respond to various changes in the preloading process in a timely manner to ensure the normal operation of the system; automated data acquisition, analysis, classification and area division reduce manual intervention and improve management efficiency; automated management processes enable the system to respond and adjust quickly, improving the overall management effect; through precise data analysis and optimized operation methods, the preloading effect can be effectively improved to ensure that the preloading process meets the preset standards; the enhanced preloading effect helps to improve the quality of products or projects and meet expected requirements; by optimizing resource allocation and utilization, resource waste can be avoided and costs can be saved; adopting different operation strategies for different types of areas helps to maximize resource utilization efficiency.

[0110] The calculation compensation parameters and the calculation adjustment parameters described in the present invention have two functions: one is to balance the left and right dimensions of the formula, and the other is to adjust the numerical results. No specific assignment is performed in this embodiment. In addition, the calculation formulas in this embodiment are used to intuitively reflect the adjustment relationship between the numerical values, such as positive correlation and negative correlation. Unless otherwise specified, the parameter values ​​that are not specifically limited to numerical values ​​are all positive.

[0111] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0112] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A vacuum preloading soil vacuum degree monitoring system, characterized in that: include: Several collection modules for collecting the actual water discharged by each drainage board; Several vacuum sensors for monitoring the actual vacuum level in each drain plate; A first comparison module, which is used to compare the single actual moisture content with the actual vacuum degree to determine the actual single-point pre-pressure condition of each monitoring point; A generation module, which is used to generate a spatial humidity distribution curve of a target area according to each individual actual moisture; A second comparison module, which is used to analyze according to the spatial humidity distribution curve to determine the pre-pressure abnormality level of the target area; A marking module, which is used to classify and mark each monitoring point according to a single actual moisture change combined with the preload abnormality level; A division module is used to divide the area according to the marking type of each monitoring point and determine the actual area of ​​each type of area; A third comparison module is used to analyze the actual area to determine the actual preload evaluation value in each type of area; A regulating module, which is used to determine the actual operation mode of each vacuum pump according to the actual pre-pressure evaluation value; The target area is monitored in real time to determine the pre-pressure conditions of each monitoring point in the target area; a set analysis is performed based on the actual parameters monitored, and the marking type of the target area is determined based on the set analysis result; and the target area is divided into regions based on the determined classification marking result, and the actual operation mode of each vacuum pump is determined based on the region division result; Each drain plate is recorded as a different monitoring point, and the acquisition module and the vacuum sensor are provided for any monitoring point; The first comparison module determines, based on the first ratio and the preset first evaluation value, that the actual single-point preloading condition of the monitoring point meets the preset standard; Wherein, the first ratio is the ratio between the single actual moisture monitored by the acquisition module at the monitoring point and the actual vacuum degree monitored by the vacuum degree sensor; If the first ratio is less than or equal to the first evaluation value, the first comparison module determines that the actual single-point preloading condition of the monitoring point meets the preset standard; If the first ratio is greater than the first evaluation value, the first comparison module determines that the actual single-point preload condition of the monitoring point does not meet the preset standard, and determines that the monitoring point meets the single alarm condition; The second comparison module determines the number of items that meet the single alarm condition at each monitoring point according to the spatial humidity distribution curve; and can determine the pre-pressure abnormality level according to the number of items that meet the single alarm condition; If the number of items that meet the single alarm condition in each monitoring point is 0, the second comparison module determines that the actual overall pre-pressure condition meets the preset standard.

2. The vacuum degree monitoring system for vacuum preloading soil according to claim 1 is characterized in that: If the number of items that meet the single alarm condition in each monitoring point is not 0 and is unique, the second comparison module determines that the actual overall preloading condition does not meet the preset standard, the preloading abnormality level is level one, and sends a first maintenance signal to perform maintenance on the vacuum preloading equipment; If the number of items that meet the single alarm condition in each monitoring point is not 0 and is not unique, the second comparison module determines that the actual overall preloading condition does not meet the preset standard, the preloading abnormality level is level 2, and a first alarm signal is issued to shut down and re-arrange the vacuum preloading equipment.

3. The vacuum degree monitoring system for vacuum preloading soil according to claim 2 is characterized in that: The marking module is provided with a collection cycle, and a single actual moisture change amount in any collection cycle is obtained according to the collection cycle; The marking module calculates a second difference absolute value based on the single actual moisture change and the preset single standard moisture change; when the actual overall pre-compression condition of the target area meets the preset standard or the pre-compression abnormality level is level one; determines the marking type based on the second difference absolute value combined with a preset second evaluation value.

4. The vacuum degree monitoring system for vacuum preloading soil according to claim 3 is characterized in that: If the absolute value of the second difference is less than or equal to the second evaluation value, the marking module marks the monitoring point as a normal type; The second absolute value of the difference is the absolute value of the difference between the single actual moisture change and the single standard moisture change.

5. The vacuum degree monitoring system for vacuum preloading soil according to claim 4 is characterized in that: If the absolute value of the second difference is greater than the second evaluation value and the single actual moisture change is less than the single standard moisture change, the marking module marks the monitoring point as a dry type; If the absolute value of the second difference is greater than the second evaluation value and the single actual moisture change is greater than the single standard moisture change, the marking module marks the monitoring point as a wet type.

6. The vacuum degree monitoring system for vacuum preloading soil according to claim 5, characterized in that: The third comparison module integrates the marker types of each monitoring point; calculates the actual pre-pressure evaluation value according to the integration result; and determines the actual operation mode of each vacuum pump according to the actual pre-pressure evaluation value; If the actual pre-pressure evaluation value is greater than or equal to the preset standard pre-pressure evaluation value, it is determined that the actual operation mode of each vacuum pump is: distributed operation mode; If the actual pre-pressure evaluation value is less than the standard pre-pressure evaluation value, it is determined that the actual operation mode of each vacuum pump is a fully open operation mode.

7. A method for monitoring the vacuum degree of a vacuum preloading soil body, based on the vacuum degree monitoring system for a vacuum preloading soil body according to any one of claims 1 to 6, characterized in that: include: Step S1, obtaining pre-compression data of each monitoring point in the target area, and determining whether the pre-compression condition of each monitoring point meets a preset standard; Step S2, analyzing the preloading data of each monitoring point, and classifying and marking each monitoring point according to the analysis results; Step S3, dividing the target area into regions according to the classification and labeling results, and determining the actual operation mode of each vacuum pump according to the region division results.

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