Thermal pressure flow monitoring system based on structured grid graph

Abstract

The present invention discloses a thermal pressure flow monitoring system based on a structured grid graph, which relates to the field of thermal pressure monitoring and includes a monitoring center. The monitoring center is communicatively linked with a data acquisition module, a model construction module, a data analysis module and a data feedback module; the data acquisition module is used to collect data on the thermal pressure flow process of a target underground building to obtain corresponding thermal pressure data; the model construction module is used to construct a model based on the collected thermal pressure data to obtain a corresponding grid graph model; the data analysis module is used to perform data analysis on the corresponding thermal pressure flow process based on the constructed grid graph model, and judge whether there is an abnormality in the corresponding thermal pressure flow process based on the analysis result, and if there is an abnormality, generate a corresponding abnormality warning; the data feedback module is used to provide feedback on the corresponding thermal pressure flow process based on the generated abnormality warning; the present invention improves the efficiency and accuracy of monitoring work.

Description

Technical Field

[0001] The present invention relates to the field of thermal pressure monitoring, and in particular to a thermal pressure flow monitoring system based on a structured grid graph. Background Art

[0002] Natural ventilation driven by temperature differences is widely used in underground buildings. These include underground substations, parking lots, subway stations, underground hydropower stations, mines, etc. These underground buildings are highly favored in order to save land and energy, make full use of underground space, and meet the production and living needs of the above-mentioned functional facilities.

[0003] Compared with existing technologies, traditional thermocompression flow monitoring systems mainly rely on decentralized sensors and simple data recording devices. These systems can usually only provide measurement data at a limited number of points, making it difficult to fully cover the entire thermocompression flow process, resulting in blind spots in monitoring. These are the problems we need to solve, and for this purpose, we provide a thermocompression flow monitoring system based on structured grid graphs. Summary of the Invention

[0004] The object of the present invention is to provide a thermal pressure flow monitoring system based on a structured grid graph.

[0005] The object of the present invention can be achieved by the following technical solution: a thermal pressure flow monitoring system based on a structured grid graph includes a monitoring center, wherein the monitoring center is communicatively linked to a data acquisition module, a model building module, a data analysis module, and a data feedback module;

[0006] The data acquisition module is used to collect data on the thermal pressure flow process of the target underground building to obtain corresponding thermal pressure data;

[0007] The model building module is used to build a corresponding regional building model based on the collected heat pressure data, and perform grid processing on the corresponding regional building model to obtain a corresponding grid graph model;

[0008] The data analysis module is used to perform data analysis on the corresponding hot-pressing flow process based on the constructed grid graph model, and determine whether there is any abnormality in the corresponding hot-pressing flow process based on the analysis results. If there is any abnormality, a corresponding abnormality warning is generated;

[0009] The data feedback module is used to provide feedback on the corresponding hot-pressing flow process based on the generated abnormal warning.

[0010] Furthermore, the data acquisition module collects data on the thermal pressure flow process of the target underground building, and the process of obtaining corresponding thermal pressure data includes:

[0011] The data acquisition module is provided with a number of data acquisition nodes, and based on the data acquisition nodes, the thermal pressure flow information at the monitoring points in the corresponding underground building is collected to obtain the corresponding thermal pressure data, which includes temperature, air pressure and flow rate; after the collection is completed, the collected thermal pressure flow information is uploaded to the corresponding monitoring center and stored.

[0012] Furthermore, the process of the model building module building a corresponding regional building model based on the collected thermal pressure data includes:

[0013] Obtaining construction drawings corresponding to the corresponding underground building, and obtaining physical connections between rooms and tunnels in the corresponding underground building based on the construction drawings. At the same time, dividing the target underground building into regions based on the construction drawings to obtain a plurality of sub-regions;

[0014] Setting a capture terminal to collect relevant building parameters in the corresponding sub-area based on the capture terminal; constructing a regional twin model corresponding to the corresponding sub-area based on the digital twin technology and in combination with the collected building parameters;

[0015] Based on the obtained physical connection relationship, the regional twin models corresponding to all the obtained sub-regions are combined to obtain the corresponding regional twin model.

[0016] Furthermore, the process of obtaining the corresponding grid graph model includes:

[0017] Constructing a two-dimensional rectangular coordinate system, and mapping the collected thermal pressure data into the corresponding two-dimensional rectangular coordinate system to obtain a corresponding thermal pressure change curve, wherein the thermal pressure change curve includes but is not limited to a temperature change curve, an air pressure change curve, and a flow rate change curve;

[0018] Obtaining a flow velocity change curve corresponding to a corresponding monitoring point in a corresponding sub-area, and obtaining a flow velocity characteristic corresponding to the corresponding monitoring point and a flow velocity change characteristic between adjacent monitoring points based on the flow change curve;

[0019] At the same time, the temperature change curves of all monitoring points in the corresponding sub-area are obtained, and based on the temperature change characteristics and heat source positions of the corresponding monitoring points are obtained;

[0020] Based on the flow velocity characteristics corresponding to the monitoring points and the flow velocity variation characteristics between adjacent monitoring points, the regional twin model corresponding to the corresponding sub-region is subjected to structured grid processing in combination with structured grid technology, and the regional twin model after the structured grid processing is subjected to virtual processing to obtain the corresponding grid unit;

[0021] Then, based on the physical connection relationship, the grid units corresponding to all sub-areas are connected to obtain a corresponding initial grid model;

[0022] The obtained heat source position is mapped to the constructed initial grid model; and the corresponding initial grid model is calibrated based on the heat source position to obtain a corresponding grid graph model; the grid graph model is composed of a number of connected sub-units.

[0023] Furthermore, the process of the data analysis module performing data analysis on the corresponding hot-pressing flow process based on the constructed grid graph model includes:

[0024] Reading the collected heat pressure data, and obtaining the corresponding heat source position and heat source intensity based on the heat pressure data;

[0025] Based on CFD simulation technology, data simulation is performed in combination with the collected thermal pressure data, heat source location, and heat source intensity to obtain the corresponding simulated heat source information;

[0026] At the same time, the collected heat pressure data is input into the constructed grid model for simulation to obtain the corresponding simulated heat source information;

[0027] Calculate the deviation values of the corresponding simulated heat source information and the simulated heat source information and the various index data in the collected heat pressure data to obtain the corresponding simulated deviation values and the simulated deviation values;

[0028] Construct a two-dimensional rectangular coordinate system with time as the horizontal axis and deviation value as the vertical axis, map the corresponding simulation deviation value and the simulated deviation value into the corresponding two-dimensional rectangular coordinate system, and obtain the corresponding deviation scatter plot;

[0029] Based on the statistical method, the corresponding simulation scatter plot and the scatter point parameters corresponding to the simulation scatter plot are obtained respectively.

[0030] Furthermore, the process of determining whether there is an abnormality in the corresponding hot-pressing flow process based on the analysis results and generating a corresponding abnormality warning if an abnormality exists includes:

[0031] Set the scatter point threshold. If all parameters in the scatter point parameters meet the scatter point threshold, no other operations will be performed.

[0032] If at least one of the parameters in the scatter point parameters does not meet the scatter point threshold, performing error estimation based on the grid graph model to obtain a corresponding error estimation coefficient;

[0033] Using the same method as above to obtain the error estimation coefficients, obtain the error estimation coefficients corresponding to all sub-units;

[0034] Set the error standard. If the error estimation coefficient meets the error standard, it indicates that there is an abnormality in the thermal pressure flow process of the corresponding underground building; if an abnormality warning is generated;

[0035] If at least one of the error estimation coefficients does not meet the error standard, the error estimation coefficient will be fed back to the monitoring center, and the management personnel in the monitoring center will correct the corresponding grid diagram model based on the error estimation coefficient. After the correction is completed, the simulation will be performed again to obtain new simulated thermal pressure information, and the above-mentioned scatter point parameter acquisition process will be repeated, and so on.

[0036] Furthermore, the process of obtaining the error estimation coefficient includes:

[0037] Obtain a subunit i in the corresponding grid model, and at the same time, obtain an adjacent subunit j that is coplanar with the corresponding subunit i;

[0038] The grid size items in the corresponding sub-unit i and sub-unit j are increased, and pseudo-gradient coefficients are constructed; and corresponding error estimation coefficients are obtained based on the obtained pseudo-gradient coefficients.

[0039] Furthermore, the process of the data feedback module providing feedback on the corresponding hot-pressing flow process based on the generated abnormal warning includes:

[0040] Obtain the generated abnormal warning, mark the subunits whose corresponding error estimation coefficients do not meet the error standard and whose scatter point parameters do not meet the scatter point threshold in red based on the abnormal warning, and feed back to the monitoring center;

[0041] Building a thermal pressure learning library based on big data, which stores historical thermal pressure control strategies for corresponding underground buildings and historical thermal pressure data corresponding to the corresponding control processes;

[0042] Numerical simulation is performed based on CFD simulation technology to construct an initial thermal pressure database, wherein the initial thermal pressure database is continuously updated with the data stored in the thermal pressure learning library to obtain a thermal pressure database;

[0043] The thermal pressure data corresponding to the corresponding sub-unit is transmitted to the corresponding thermal pressure database, and the corresponding simulated thermal pressure information and the corresponding thermal pressure adjustment strategy are obtained through the thermal pressure control strategy and the thermal pressure change curve in the thermal pressure database; the corresponding simulated thermal pressure information is compared with the historical thermal pressure data in the thermal pressure learning library, and the simulated thermal pressure data is corrected according to the historical thermal pressure data, and then the thermal pressure adjustment strategy corresponding to the corresponding simulated thermal pressure information is corrected according to the correction result. After the correction is completed, the final thermal pressure adjustment strategy is obtained and fed back to the monitoring center.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. A grid model that is highly consistent with the actual underground structure was constructed; this enables the data analysis module to perform data analysis based on a precise model, thereby more accurately determining whether there are anomalies in the thermal pressure flow process and generating anomaly warnings in a timely manner, effectively improving the efficiency and accuracy of anomaly response;

[0046] 2. It not only provides abnormal warnings, but also provides managers with thermal pressure adjustment strategies based on big data and CFD simulation technology to promote the optimization of the internal environment of underground buildings. At the same time, by establishing a thermal pressure learning library and a thermal pressure database, it can continuously learn and update, thereby improving the accuracy of predicting and regulating future thermal pressure flow states. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0048] like Figure 1 As shown, a thermal pressure flow monitoring system based on a structured grid diagram includes a monitoring center, wherein the monitoring center is communicatively linked to a data acquisition module, a model building module, a data analysis module, and a data feedback module;

[0049] The data acquisition module is used to collect data on the thermal pressure flow process of the target underground building to obtain corresponding thermal pressure data;

[0050] The model building module is used to build a corresponding regional building model based on the collected heat pressure data, and perform grid processing on the corresponding regional building model to obtain a corresponding grid graph model;

[0051] The data analysis module is used to perform data analysis on the corresponding hot-pressing flow process based on the constructed grid graph model, and determine whether there is any abnormality in the corresponding hot-pressing flow process based on the analysis results. If there is any abnormality, a corresponding abnormality warning is generated;

[0052] The data feedback module is used to provide feedback on the corresponding hot-pressing flow process based on the generated abnormal warning;

[0053] It should be further explained that, in a specific implementation process, the data acquisition module collects data on the thermal pressure flow process of the target underground building, and the process of obtaining the corresponding thermal pressure data includes:

[0054] The data acquisition module is provided with a plurality of data acquisition nodes, which are composed of a plurality of Internet of Things terminals with different functions, including but not limited to temperature terminals, pressure terminals and flow rate terminals;

[0055] The data acquisition nodes are deployed at monitoring points within the target underground building area, and the thermal pressure flow information at the corresponding monitoring points is collected based on the data acquisition nodes to obtain corresponding thermal pressure data, which includes temperature, air pressure, and flow rate. The monitoring points are selected by the staff based on actual needs. For example, the monitoring points can be tunnels within the corresponding underground building, connecting areas between rooms, or air outlets and air inlet areas within the corresponding tunnels and rooms.

[0056] After the collection is completed, the collected thermal pressure flow information is uploaded to the corresponding monitoring center and stored;

[0057] It should be further explained that, in the specific implementation process, the data acquisition node is set with fixed acquisition frequency, acquisition period and other related acquisition parameters.

[0058] It should be further explained that, in a specific implementation process, the process of the model building module building a corresponding regional building model based on the collected thermal pressure data includes:

[0059] Obtaining construction drawings corresponding to the corresponding underground building, and obtaining physical connections between rooms and tunnels in the corresponding underground building based on the construction drawings. At the same time, dividing the target underground building into regions based on the construction drawings to obtain a plurality of sub-regions;

[0060] Setting a capture terminal, and collecting relevant building parameters in the corresponding sub-area based on the capture terminal, wherein the building parameters include relevant data such as the length, volume, and geometric shape of the corresponding sub-area;

[0061] Based on digital twin technology and combined with the obtained building parameters, a regional twin model corresponding to the corresponding sub-area is constructed;

[0062] Furthermore, based on the obtained physical connection relationship, the regional twin models corresponding to all the obtained sub-regions are combined to obtain the corresponding regional twin model;

[0063] It should be further explained that, in the specific implementation process, the process of obtaining the corresponding grid graph model includes:

[0064] Reading the collected thermal pressure data, constructing a two-dimensional rectangular coordinate system with respect to the thermal pressure data in time, obtaining a corresponding two-dimensional rectangular coordinate system, and mapping the collected thermal pressure data into the corresponding two-dimensional rectangular coordinate system to obtain a corresponding thermal pressure change curve, the thermal pressure change curve including but not limited to a temperature change curve, an air pressure change curve, and a flow rate change curve;

[0065] Taking a certain sub-region as an example, obtain the flow velocity change curve corresponding to the corresponding monitoring point in the corresponding sub-region, and obtain the flow velocity characteristics corresponding to the corresponding monitoring point and the flow velocity change characteristics between adjacent monitoring points based on the flow change curve;

[0066] At the same time, the temperature change curves of all monitoring points in the corresponding sub-region are obtained, and the temperature change characteristics of the corresponding monitoring points are obtained based on the temperature change characteristics of the corresponding monitoring points. Then, based on the temperature change characteristics of the corresponding monitoring points, it is judged whether there is a high-temperature area in the corresponding sub-region. If not, no other operations are performed; if there is a high-temperature area, the corresponding high-temperature position is determined based on the temperature change characteristics and marked as the heat source position;

[0067] Then, based on the flow velocity characteristics corresponding to the monitoring points and the flow velocity variation characteristics between adjacent monitoring points, the regional twin model corresponding to the corresponding sub-region is subjected to structured grid processing in combination with structured grid technology, and the regional twin model after structured grid processing is subjected to virtual processing to obtain the corresponding grid unit;

[0068] Then, based on the physical connection relationship, the grid units corresponding to all sub-areas are connected to obtain a corresponding initial grid model;

[0069] Furthermore, the obtained heat source position is mapped to the constructed initial grid model; and the grid units in the corresponding initial grid model are recalibrated based on the heat source position. After the calibration is completed, a corresponding grid graph model is obtained, wherein the grid graph model is composed of a plurality of connected sub-units, wherein the sub-units are recalibrated grid units, and each sub-unit has only two monitoring points;

[0070] It should be further explained that, in a specific implementation process, the data analysis module performs data analysis on the corresponding hot-pressing flow process based on the constructed grid graph model, and determines whether there is an abnormality in the corresponding hot-pressing flow process based on the analysis results. If an abnormality exists, the process of generating a corresponding abnormality warning includes:

[0071] Reading the collected heat pressure data, and obtaining the heat source position and heat source intensity in the corresponding underground building group based on the temperature data in the heat pressure data;

[0072] Based on CFD simulation technology, data simulation is performed in combination with the collected thermal pressure data and the obtained heat source position and heat source intensity to obtain the corresponding simulated heat source information;

[0073] At the same time, the collected heat pressure data is input into the constructed grid model for simulation to obtain the corresponding simulated heat source information;

[0074] Calculate the deviation values of the corresponding simulated heat source information and the simulated heat source information and the various index data in the collected heat pressure data to obtain the corresponding simulated deviation values and the simulated deviation values;

[0075] Constructing a two-dimensional rectangular coordinate system with time as the horizontal axis and the deviation value as the vertical axis, mapping the corresponding simulation deviation value and the simulated deviation value into the corresponding two-dimensional rectangular coordinate system, respectively, to obtain a corresponding deviation scatter plot, wherein the deviation scatter plot includes a simulation scatter plot and a simulation scatter plot; the simulation scatter plot and the simulation scatter plot both include a temperature scatter plot, an air pressure scatter plot, and a flow rate scatter plot;

[0076] Based on statistical methods, the corresponding simulation scatter plot and the scatter point parameters corresponding to the simulation scatter plot are respectively obtained, wherein the scatter point parameters include mean square error, root mean square error, and correlation coefficient; wherein the correlation coefficient is an indicator for measuring the strength and direction of the relationship between the simulation scatter plot and the indicator data at the same time in the simulation scatter plot; it is mainly used to describe the linear relationship between the indicator data;

[0077] Set the scatter point threshold. If all parameters in the scatter point parameters meet the scatter point threshold, no other operations will be performed.

[0078] If at least one of the parameters in the scatter point parameters does not meet the scatter point threshold, an error estimation is performed based on the grid graph model. The corresponding error estimation process is as follows:

[0079] Obtaining the constructed grid model and mapping the collected thermal pressure data to the sub-units in the corresponding grid model;

[0080] Select any subunit in the corresponding grid model and mark it as subunit i. At the same time, obtain the adjacent subunits coplanar with the corresponding subunit i and mark them as j, where j = 1, 2, ..., n, n > 0, and n is a constant, representing the total number of subunits adjacent to the subunit i.

[0081] The grid size items in the corresponding subunits i and j are increased, and the pseudo-gradient coefficients are constructed. The corresponding construction formula is as follows: Q ij =|qi-qj|×(D ij ) l Where Qij represents the pseudo-gradient coefficient, qi and qj represent the flow change values corresponding to the corresponding subunit i and subunit j, respectively, and the flow change value corresponds to the data deviation value (such as temperature deviation) at the monitoring point in the corresponding subunit;

[0082] The error estimation coefficient of subunit i is obtained based on the shock wave identification method. The corresponding calculation formula is as follows: ei=max(Q ij );

[0083] The same method as above is used to obtain the error estimation coefficients to obtain the error estimation coefficients corresponding to all sub-units;

[0084] Set the error standard. If the error estimation coefficient meets the error standard, it indicates that there is an abnormality in the thermal pressure flow process of the corresponding underground building; if an abnormality warning is generated;

[0085] If at least one of the error estimation coefficients does not meet the error standard, the error estimation coefficient is fed back to the monitoring center, and the management personnel in the monitoring center correct the corresponding grid model based on the error estimation coefficient. After the correction is completed, the simulation is performed again to obtain new simulated thermal pressure information, and the above-mentioned scatter point parameter acquisition process is repeated, and so on;

[0086] It should be further explained that, in a specific implementation process, the process in which the data feedback module is used to provide feedback on the corresponding hot-pressing flow process based on the generated abnormal warning includes:

[0087] Obtain the generated abnormal warning, and based on the abnormal warning, mark the subunits whose corresponding error estimation coefficients do not meet the error standard and whose scatter point parameters do not meet the scatter point threshold in red, so as to facilitate the corresponding staff to issue an early warning;

[0088] Building a thermal pressure learning library based on big data, which stores historical thermal pressure control strategies for corresponding underground buildings and historical thermal pressure data corresponding to the corresponding control processes;

[0089] Furthermore, numerical simulation is performed based on CFD simulation technology to construct an initial thermal pressure database. The initial thermal pressure database is continuously updated with the data stored in the thermal pressure learning library to obtain a thermal pressure database; the thermal pressure database stores several thermal pressure control strategies and corresponding thermal pressure change curves;

[0090] The thermal pressure data corresponding to the corresponding sub-unit is transmitted to the corresponding thermal pressure database, and the corresponding simulated thermal pressure information and the corresponding thermal pressure adjustment strategy are obtained through the thermal pressure control strategy and the thermal pressure change curve in the thermal pressure database; the corresponding simulated thermal pressure information is compared with the historical thermal pressure data in the thermal pressure learning library, and the simulated thermal pressure data is corrected according to the historical thermal pressure data, and then the thermal pressure adjustment strategy corresponding to the corresponding simulated thermal pressure information is corrected according to the correction result. After the correction is completed, the final thermal pressure adjustment strategy is obtained and fed back to the monitoring center so that the staff can adjust the sub-unit with abnormal thermal pressure flow state processing.

[0091] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The thermal pressure flow monitoring system based on structured grid graph is characterized by: It includes a monitoring center, wherein the monitoring center is connected to a data acquisition module, a model building module, a data analysis module and a data feedback module; The data acquisition module is used to collect data on the thermal pressure flow process of the target underground building to obtain corresponding thermal pressure data; The model building module is used to build a corresponding regional building model based on the collected heat pressure data, and perform grid processing on the corresponding regional building model to obtain a corresponding grid graph model; The data analysis module is used to perform data analysis on the corresponding hot-pressing flow process based on the constructed grid graph model, and determine whether there is any abnormality in the corresponding hot-pressing flow process based on the analysis results. If there is any abnormality, a corresponding abnormality warning is generated; The data feedback module is used to provide feedback on the corresponding hot-pressing flow process based on the generated abnormal warning; The data acquisition module collects data on the thermal pressure flow process of the target underground building, and the process of obtaining corresponding thermal pressure data includes: The data acquisition module is provided with a plurality of data acquisition nodes, and based on the data acquisition nodes, the thermal pressure flow information at the monitoring points in the corresponding underground building is collected to obtain the corresponding thermal pressure data, which includes temperature, air pressure and flow rate. After the collection is completed, the collected thermal pressure flow information is uploaded to the corresponding monitoring center and stored; The process of constructing a corresponding regional building model based on the collected thermal pressure data by the model construction module includes: Obtaining construction drawings corresponding to the corresponding underground building, and obtaining physical connections between rooms and tunnels in the corresponding underground building based on the construction drawings. At the same time, dividing the target underground building into regions based on the construction drawings to obtain a plurality of sub-regions; Setting a capture terminal to collect relevant building parameters in the corresponding sub-area based on the capture terminal; constructing a regional twin model corresponding to the corresponding sub-area based on the digital twin technology and in combination with the collected building parameters; Based on the obtained physical connection relationship, the regional twin models corresponding to all the obtained sub-regions are combined to obtain the corresponding regional twin model; The process of obtaining the corresponding grid graph model includes: Constructing a two-dimensional rectangular coordinate system, and mapping the collected thermal pressure data into the corresponding two-dimensional rectangular coordinate system to obtain a corresponding thermal pressure change curve, wherein the thermal pressure change curve includes a temperature change curve, an air pressure change curve, and a flow rate change curve; Obtaining a flow velocity change curve corresponding to a corresponding monitoring point in a corresponding sub-area, and obtaining a flow velocity characteristic corresponding to the corresponding monitoring point and a flow velocity change characteristic between adjacent monitoring points based on the flow velocity change curve; At the same time, the temperature change curves of all monitoring points in the corresponding sub-area are obtained, and based on the temperature change characteristics and heat source positions of the corresponding monitoring points are obtained; Based on the flow velocity characteristics corresponding to the monitoring points and the flow velocity variation characteristics between adjacent monitoring points, the regional twin model corresponding to the corresponding sub-region is subjected to structured grid processing in combination with structured grid technology, and the regional twin model after the structured grid processing is subjected to virtual processing to obtain the corresponding grid unit; Then, based on the physical connection relationship, the grid units corresponding to all sub-areas are connected to obtain a corresponding initial grid model; The obtained heat source position is mapped to the constructed initial grid model; and the corresponding initial grid model is calibrated based on the heat source position to obtain a corresponding grid graph model; the grid graph model is composed of a number of connected sub-units.

2. The thermal pressure flow monitoring system based on structured grid graph according to claim 1, characterized in that: The process of the data analysis module performing data analysis on the corresponding hot-pressing flow process based on the constructed grid graph model includes: Reading the collected heat pressure data, and obtaining the corresponding heat source position and heat source intensity based on the heat pressure data; Based on CFD simulation technology, data simulation is performed in combination with the collected thermal pressure data, heat source location, and heat source intensity to obtain the corresponding simulated heat source information; At the same time, the collected heat pressure data is input into the constructed grid model for simulation to obtain the corresponding simulated heat source information; Calculate the deviation values of the corresponding simulated heat source information and the simulated heat source information and the various index data in the collected heat pressure data to obtain the corresponding simulated deviation values and the simulated deviation values; Construct a two-dimensional rectangular coordinate system with time as the horizontal axis and deviation value as the vertical axis, map the corresponding simulation deviation value and the simulated deviation value into the corresponding two-dimensional rectangular coordinate system, and obtain the corresponding deviation scatter plot; Based on the statistical method, the corresponding simulation scatter plot and the scatter point parameters corresponding to the simulation scatter plot are obtained respectively.

3. The thermal pressure flow monitoring system based on structured grid graph according to claim 2, characterized in that: Based on the analysis results, it is determined whether there is any abnormality in the corresponding hot-pressing flow process. If there is an abnormality, the process of generating a corresponding abnormality warning includes: Set the scatter point threshold. If all parameters in the scatter point parameters meet the scatter point threshold, no other operations will be performed. If at least one of the parameters in the scatter point parameters does not meet the scatter point threshold, performing error estimation based on the grid graph model to obtain a corresponding error estimation coefficient; The same method as above is used to obtain the error estimation coefficients to obtain the error estimation coefficients corresponding to all sub-units; Set the error standard. If the error estimation coefficient meets the error standard, it indicates that there is an abnormality in the thermal pressure flow process of the corresponding underground building; if an abnormality warning is generated; If at least one of the error estimation coefficients does not meet the error standard, the error estimation coefficient will be fed back to the monitoring center, and the management personnel in the monitoring center will correct the corresponding grid diagram model based on the error estimation coefficient. After the correction is completed, the simulation will be performed again to obtain new simulated thermal pressure information, and the above-mentioned scatter point parameter acquisition process will be repeated, and so on.

4. The thermal pressure flow monitoring system based on structured grid graph according to claim 3, characterized in that: The process of obtaining the error estimate coefficients includes: Obtain a subunit i in the corresponding grid model, and at the same time, obtain an adjacent subunit j that is coplanar with the corresponding subunit i; The grid size items in the corresponding sub-unit i and sub-unit j are increased, and pseudo-gradient coefficients are constructed; and corresponding error estimation coefficients are obtained based on the obtained pseudo-gradient coefficients.

5. The thermal pressure flow monitoring system based on structured grid graph according to claim 3, characterized in that: The process of the data feedback module for providing feedback on the corresponding hot-pressing flow process based on the generated abnormal warning includes: Obtain the generated abnormal warning, mark the subunits whose corresponding error estimation coefficients do not meet the error standard and whose scatter point parameters do not meet the scatter point threshold in red based on the abnormal warning, and feed back to the monitoring center; Building a thermal pressure learning library based on big data, which stores historical thermal pressure control strategies for corresponding underground buildings and historical thermal pressure data corresponding to the corresponding control processes; Numerical simulation is performed based on CFD simulation technology to construct an initial thermal pressure database, wherein the initial thermal pressure database is continuously updated with the data stored in the thermal pressure learning library to obtain a thermal pressure database; The thermal pressure data corresponding to the corresponding sub-unit is transmitted to the corresponding thermal pressure database, and the corresponding simulated thermal pressure information and the corresponding thermal pressure adjustment strategy are obtained through the thermal pressure control strategy and the thermal pressure change curve in the thermal pressure database; the corresponding simulated thermal pressure information is compared with the historical thermal pressure data in the thermal pressure learning library, and the simulated thermal pressure data is corrected according to the historical thermal pressure data, and then the thermal pressure adjustment strategy corresponding to the corresponding simulated thermal pressure information is corrected according to the correction result. After the correction is completed, the final thermal pressure adjustment strategy is obtained and fed back to the monitoring center.

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