A vertical freezing and thawing management system based on data prediction

Abstract

The present invention relates to the technical field of construction management, and specifically discloses a vertical freezing and thawing management system based on data prediction, including an analysis and prediction module, which standardizes multiple parameters obtained by a first monitoring unit and obtains a state coefficient based on the standardized parameters. The analysis and prediction module builds a prediction model based on multiple data obtained by a second monitoring unit, and uses it to judge the suitability of the freezing temperature. The present invention predicts the state of structural parts that have not exceeded a critical state by matching and combining freezing data with affected structural parts data, and conversely guides the vertical freezing and thawing process of freezing construction, so that freezing construction under special geographical conditions rich in water and mud can better protect underground buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of construction management, and in particular to a vertical freezing and thawing management system based on data prediction. Background Art

[0002] During underground construction using a shield machine, the soil needs to be reinforced in some cases. Generally speaking, the reinforcement scheme can be a grouting reinforcement scheme or a freezing reinforcement scheme. The two schemes need to be compared and analyzed based on the actual geological conditions of the construction site. Generally speaking, the grouting reinforcement scheme requires large-section dark excavation, which is not applicable when there are water-rich sand layers or plots that need to be protected. Therefore, when using the freezing reinforcement scheme, since the freezing reinforcement scheme will cause certain damage to the nearby geographical features during the freezing process, this method needs to be monitored and managed during the use of the freezing method.

[0003] At present, the monitoring method of the freezing reinforcement scheme is to arrange multiple pressure relief holes inside each obstacle clearance channel in the closed area of ​​the freezing curtain, and then monitor the pressure in the pressure relief holes to monitor the pressure changes in the freezing curtain to determine the formation of the freezing curtain, and the pressure relief holes can assist in releasing the frost heave pressure. This monitoring and management method can meet the needs of conventional construction to a certain extent by judging through the pressure data of the day. However, for areas with underground buildings, such as subway stations, conventional monitoring and management are difficult to achieve precise control of the freezing curtain, especially the freezing in the vertical direction. Once the freezing curtain develops excessively and causes the underground building to approach or enter the freezing range and cause damage, additional construction is required, resulting in unnecessary cost increases.

[0004] In view of this, the present invention proposes a vertical freezing and thawing management system based on data prediction, which takes into account underground building structures close to critical conditions and increases the accuracy of monitoring and management during the freezing and thawing process by means of data prediction. Summary of the invention

[0005] The purpose of the present invention is to provide a vertical freezing and thawing management system based on data prediction to solve the following technical problems:

[0006] How to manage the freezing and thawing process in a construction area with underground buildings that requires freezing construction to ensure the construction effect and the safety of the underground buildings.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A vertical freezing and thawing management system based on data prediction, comprising:

[0009] The monitoring module, analysis and prediction module and control module are characterized by:

[0010] The monitoring module includes a first monitoring unit and a second monitoring unit, wherein the first monitoring unit is arranged in a freezing area, wherein a plurality of freezing holes, temperature measuring holes, temperature control holes and pressure relief holes are arranged in the freezing area, wherein the first monitoring unit is used to obtain the freezing temperature in the freezing holes, the first temperature of the temperature measuring holes, the second temperature of the temperature control holes, the freezing pressure of the pressure relief holes and the ambient temperature of the freezing area, and the second monitoring unit is arranged in an underground building outside the freezing area, and is used to obtain the third temperature and stress data of a plurality of structural members in the underground building that are affected by low temperature;

[0011] The analysis and prediction module normalizes the multiple parameters obtained by the first monitoring unit and obtains the state coefficient according to the normalized parameters. The analysis and prediction module builds a prediction model based on the multiple data obtained by the second monitoring unit and uses it to judge the suitability of the freezing temperature.

[0012] The control module is used to control the freezing temperature and set the control period. The freezing temperature in the first control period after startup is a preset value, and the freezing temperature is adjusted based on the applicability of the freezing temperature during the freezing process, the maintenance process and the thawing process.

[0013] Through the above technical scheme: it provides a method for predicting and managing the vertical freeze-thaw process of frozen construction through monitoring data. Specifically, by matching and combining the frozen data with the affected structural member data, the state of the structural members that have not exceeded the critical state is predicted, and vice versa, the vertical freeze-thaw process of the frozen construction is guided, so that the frozen construction under special geographical conditions rich in water and mud can better protect underground buildings.

[0014] As a further technical solution of the present invention: the freezing area is provided with a plurality of freezing holes, temperature measuring holes, temperature control holes and pressure relief holes, including:

[0015] The freezing holes include a freezing hole vertical to the ground and a horizontal freezing hole in the well for assisting freezing. The pressure relief holes are arranged in the well and are arranged horizontally in a plurality along the extending direction of the vertical freezing holes.

[0016] The temperature measuring holes are arranged in the weak links of freezing to measure the temperature of different parts of the freezing curtain.

[0017] As a further technical solution of the present invention: the process of obtaining the state coefficient includes:

[0018] The actual freezing temperature, the first temperatures of several temperature measuring holes, the ambient temperature and the freezing pressure of the pressure relief hole closest to the affected structural member are standardized respectively;

[0019] Perform a weighted summation on the standardized first temperatures of a plurality of temperature measuring holes to obtain a temperature measurement coefficient;

[0020] The actual freezing temperature, ambient temperature, freezing pressure and the temperature measurement coefficient after numerical standardization are subjected to a secondary weighted summation to obtain the state coefficient.

[0021] As a further technical solution of the present invention: the process of constructing the prediction model includes:

[0022] Dividing the control period into a plurality of monitoring periods, and obtaining values ​​of the third temperature and stress data in the monitoring periods;

[0023] Based on the values ​​of the third temperature and stress data, construct the function:

[0024]

[0025] in,

[0026]

[0027]

[0028] is the constructed function and for arrive A set of n is the number of independent variables, is the difference between the third temperature at the start and end of the monitoring period, is the preset standard time value, is the difference between the first cumulative amount of stress of the structure obtained in the current monitoring period and the second cumulative amount of stress of the structure obtained in the previous monitoring period. It is the preset standard cumulative value.

[0029] Through the above technical solution: a process of constructing a prediction model is provided, the prediction model obtains prediction values ​​based on the constructed function, and makes applicability judgment based on the prediction values. Specifically, the function constructed by the prediction model conforms to the data distribution of empirical statistics on the one hand, and has a large number of local maximum values, which can improve the prediction accuracy when used for comparison.

[0030] As a further technical solution of the present invention: the process of using the prediction model for judging the suitability of freezing temperature includes:

[0031] In the state coefficient function graph corresponding to several monitoring periods, the integral value of the state coefficient in the monitoring period is obtained;

[0032] Compare a number of integral values ​​with a preset reference value, and select a monitoring period in which the integral value is greater than the reference value;

[0033] From the selected monitoring time periods whose integral values ​​are greater than the reference value, select the monitoring time period in which the difference between the third temperature at the end of the time period and the critical temperature is the smallest as the target time period;

[0034] Substitute the corresponding data of the target period into the prediction model to obtain the predicted value, and make a suitability judgment based on the predicted value.

[0035] As a further technical solution of the present invention: the process of determining suitability based on the predicted value includes:

[0036] Compare the predicted value with the preset discrimination interval;

[0037] If the predicted value is on the left side of the discrimination interval, it is judged that the current freezing temperature is too low and needs to be adjusted;

[0038] If the predicted value falls within the discrimination interval, a second judgment is required;

[0039] If the predicted value is on the right side of the discrimination interval, it is judged that the current freezing temperature meets the requirements and does not need to be adjusted.

[0040] Through the above technical solution: a process of suitability judgment is provided. Through the suitability judgment, the adjustment of the freezing temperature can be reasonably controlled based on the results of the prediction model, thereby improving the control efficiency and reducing the problem of excessive influence on the construction of underground buildings and frost heave cracking.

[0041] As a further technical solution of the present invention: the process of performing secondary judgment includes:

[0042] Taking the target period as the benchmark, select several reference periods along the time axis;

[0043] Substituting the corresponding data of the reference period into the prediction model to obtain multiple prediction values;

[0044] Multiple reference points are constructed based on multiple predicted values ​​and reference period numbers, and linear regression analysis is performed on the multiple reference points to obtain the slope of the regression line.

[0045] As a further technical solution of the present invention: the process of performing the secondary judgment further includes:

[0046] The obtained regression line slope is compared with the preset slope. If the obtained regression line slope is not less than the preset slope, it is determined that the current freezing temperature meets the requirements and does not need to be adjusted.

[0047] If the slope of the obtained regression line is less than the preset slope, it is determined that the current freezing temperature is close to the critical condition and the length of the control period needs to be reduced.

[0048] Through the above technical solution: a secondary judgment process is provided, and the specific secondary judgment further subdivides and judges the special circumstances in the process of using the predicted value for suitability judgment. The basis of the secondary judgment is the changing trend of the predicted value. When the changing trend is upward, that is, the slope finally obtained is high, the change of the structural parts under the current freezing conditions is continuously improving, and there is no need to adjust the freezing temperature. On the contrary, the changing trend is downward. Since various parameters are close to the critical conditions, the control period length is shortened, and the judgment frequency of the suitability judgment of adjusting the freezing temperature is increased, thereby maximizing the protection of the structural parts of the underground building.

[0049] As a further technical solution of the present invention: a freezing pipe is arranged in the freezing hole, and the freezing pipe is connected by welding with an inner liner pipe clamp and a thread, and the tensile strength is not less than 75% of the mother pipe.

[0050] Beneficial effects of the present invention:

[0051] (1) The present invention predicts the state of structural members that have not exceeded the critical state by matching and combining freezing data with the data of affected structural members, and conversely guides the vertical freezing and thawing process of freezing construction, so that freezing construction under special geographical conditions rich in water and mud can better protect underground buildings.

[0052] (2) The prediction model of the present invention obtains prediction values ​​based on the constructed function and makes applicability judgment based on the prediction values. Specifically, the function constructed by the prediction model conforms to the data distribution of empirical statistics and has a large number of local maximum values, which can improve the prediction accuracy when used for comparison.

[0053] (3) The present invention can reasonably control the adjustment of freezing temperature based on the results of the prediction model, thereby improving the control efficiency and reducing the problem of excessive impact on the construction of underground buildings and frost heave cracking.

[0054] (4) The secondary judgment of the present invention is that when the change trend is upward, that is, the final slope is high, the change of the structural components under the current freezing conditions is continuously improving, and there is no need to adjust the freezing temperature. On the contrary, when the change trend is downward, since various parameters are close to the critical conditions, the control period length is shortened, and the judgment frequency of the applicability judgment of adjusting the freezing temperature is increased, thereby maximizing the protection of the structural components of the underground building. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described below in conjunction with the accompanying drawings.

[0056] Figure 1 It is a schematic diagram of the composition relationship of the management system modules of the present invention;

[0057] Figure 2It is a flow chart of the applicability determination steps of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 and Figure 2 As shown, in one embodiment, a vertical freezing and thawing management system based on data prediction is provided, comprising:

[0060] The monitoring module includes a first monitoring unit and a second monitoring unit. The first monitoring unit is used to monitor the freezing curtain, and the second monitoring unit is used to monitor the underground building. The first monitoring unit is arranged in the freezing area. A plurality of freezing holes, temperature measuring holes, temperature control holes and pressure relief holes are arranged in the freezing area. The first monitoring unit is used to obtain the freezing temperature in the freezing hole, the first temperature of the temperature measuring hole, the second temperature of the temperature control hole, the freezing pressure of the pressure relief hole and the ambient temperature of the freezing area. The second monitoring unit is arranged in the underground building outside the freezing area, and is used to obtain the third temperature and stress data of a plurality of structural parts in the underground building affected by low temperature. The stress data can be detected by stress patch or ultrasonic wave. The specific process is not described in detail. In order to pursue the detection area, ultrasonic monitoring is preferably used.

[0061] The analysis and prediction module normalizes the multiple parameters obtained by the first monitoring unit and obtains the state coefficient according to the normalized parameters. The analysis and prediction module builds a prediction model based on the multiple data obtained by the second monitoring unit and uses it to judge the suitability of the freezing temperature.

[0062] The control module is used to control the freezing temperature and set the control period. The freezing temperature of the first control period after startup is the preset value, and the freezing temperature is adjusted based on the applicability of the freezing temperature during the freezing process, maintenance process and thawing process.

[0063] In this embodiment, a method for predicting and managing the vertical freeze-thaw process of frozen construction by monitoring data is provided. Specifically, by matching and combining the frozen data with the affected structural member data, the state of the structural members that have not exceeded the critical state is predicted, and vice versa, the vertical freeze-thaw process of the frozen construction is guided, so that the frozen construction under special geographical conditions rich in water and mud can better protect underground buildings.

[0064] The freezing area is provided with a number of freezing holes, temperature measuring holes, temperature control holes and pressure relief holes, including:

[0065] The freezing holes include a freezing hole vertical to the ground and a horizontal freezing hole in the well for assisting freezing. The pressure relief holes are arranged in the well and are arranged horizontally in a plurality of directions along the extension of the vertical freezing holes.

[0066] The principle of arranging temperature measuring holes is to arrange them at the weak links of freezing, which are used to measure the temperature of different parts of the freezing curtain. The weak links of freezing are determined after address exploration.

[0067] For example, at a station near a river, the construction process of the freezing hole includes:

[0068] A freezing hole was constructed horizontally in the shield advancement area in the obstacle clearance well on one side. The length was about 24m (to the outside of the first underground continuous wall), and the thickness of the frozen wall was 2.5m~3.5m. After the freezing reached the design requirements, the dark excavation construction was carried out. The dark excavation section of the first 19 meters was about ￠7.7m. When it was 5 meters away from the underground continuous wall, the excavation section was expanded to ￠10.7m. The structure was supported by temporary brackets and shotcrete. The bracket spacing was 0.5m. All brackets and frozen soil bodies were tightly backed with wooden boards, and a small amount of gaps were filled with crushed soil. The mesh was hung for sprayed concrete support. The strength of the sprayed concrete was C20 and the thickness of the sprayed concrete was 500mm. The excavation length reaches the outside of the underground continuous wall, and the secondary horizontal freezing hole construction is carried out with a length of about 20.68m (to the inside of the underground continuous wall on the opposite side). The outer wall of the underground continuous wall on the opposite side adopts the vertical freezing hole constructed at the ground position of the south obstacle clearance shaft. After the freezing hole construction is completed, freezing is carried out. After the freezing meets the design requirements, the first underground continuous wall is chiseled out, and then the second underground continuous wall is excavated secretly. Then the second underground continuous wall is chiseled out. The section of the dark excavation is about ￠7m, and the support still adopts the same obstacle clearance shaft to the first underground continuous wall.

[0069] The freezing effects of the freezing holes may vary under different construction schemes. The construction of this embodiment is under conditions of extremely high humidity, so the construction standards under other similar conditions should not be lower than the standards of the above-mentioned construction process.

[0070] The process of obtaining the state coefficients includes:

[0071] The actual freezing temperature, the first temperatures of several temperature measuring holes, the ambient temperature and the freezing pressure of the pressure relief hole closest to the affected structural member are standardized respectively;

[0072] Perform a weighted summation on the standardized first temperatures of a plurality of temperature measuring holes to obtain a temperature measurement coefficient;

[0073] The actual freezing temperature, ambient temperature, freezing pressure and temperature measurement coefficient after numerical standardization are subjected to secondary weighted summation to obtain the state coefficient. The state coefficient is used to indicate the freezing state of the freezing construction. The larger the value of the state coefficient, the better the freezing effect.

[0074] As an example, in this embodiment, the state coefficient is expressed by the formula:

[0075]

[0076] in, , , and are the weight coefficients set for freezing temperature, ambient temperature, freezing pressure and temperature measurement coefficient respectively by quadratic weighted summation. is the normalized freezing temperature, is the normalized freezing pressure, is the standardized ambient temperature, is the number of first temperatures in a weighted summation process, It is the first The weight value of the first temperature, It is the first The first temperature normalized value.

[0077] The process of building a predictive model includes:

[0078] Dividing the control period into a plurality of monitoring periods, and obtaining values ​​of the third temperature and stress data in the monitoring periods;

[0079] Based on the values ​​of the third temperature and stress data, construct the function:

[0080]

[0081] in,

[0082]

[0083]

[0084] is the constructed function and for arrive A set of n is the number of independent variables, is the difference between the third temperature at the start and end of the monitoring period, is the preset standard time value, is the difference between the first cumulative amount of stress of the structure obtained in the current monitoring period and the second cumulative amount of stress of the structure obtained in the previous monitoring period. It is the preset standard cumulative value.

[0085] Obviously, the number of independent variables in this embodiment is 2, then You can write:

[0086]

[0087] It should be noted that the status of some special structural parts will be affected by a variety of other parameters. For example, structural parts made of materials such as rubber often need to take other independent variables into consideration.

[0088] Through the above scheme, a process of constructing a prediction model is provided in this embodiment. The prediction model obtains a prediction value based on the constructed function and makes a suitability judgment based on the prediction value. Specifically, the function constructed by the prediction model conforms to the data distribution of empirical statistics on the one hand, and has a large number of local maximum values, which can improve the prediction accuracy when used for comparison.

[0089] refer to Figure 2 , the process of using the prediction model for the suitability of freezing temperature includes:

[0090] S1. In the state coefficient function graph corresponding to several monitoring periods, obtain the integral value of the state coefficient in the monitoring period;

[0091] S2. Compare a number of integral values ​​with a preset reference value, and select a monitoring period in which the integral value is greater than the reference value;

[0092] S3, from the selected monitoring time periods whose integral values ​​are greater than the reference value, select the monitoring time period in which the difference between the third temperature at the end of the time period and the critical temperature is the smallest as the target time period;

[0093] S4. Substitute the corresponding data of the target period into the prediction model to obtain the prediction value, and make a suitability judgment based on the prediction value.

[0094] The process of determining suitability based on predicted values ​​includes:

[0095] S5, comparing the predicted value with the preset discrimination interval;

[0096] If the predicted value is on the left side of the discrimination interval, it is determined that the current freezing temperature is too low and needs to be adjusted. In this embodiment, the freezing temperature adjustment process is set based on the numerical query corresponding table of the predicted value, and the freezing process, the maintenance process and the thawing process use different preset tables;

[0097] If the predicted value falls within the discrimination interval, a second judgment is required;

[0098] If the predicted value is on the right side of the discrimination interval, it is judged that the current freezing temperature meets the requirements and does not need to be adjusted.

[0099] In this embodiment, a process of suitability judgment is provided. Through the suitability judgment, the adjustment of the freezing temperature can be reasonably controlled based on the results of the prediction model, thereby improving the control efficiency and reducing the problem of excessive influence on the construction of underground buildings and frost heave cracking.

[0100] The process of making a secondary judgment includes:

[0101] Taking the target period as the benchmark, select several reference periods along the time axis;

[0102] Substituting the corresponding data of the reference period into the prediction model to obtain multiple prediction values;

[0103] Multiple reference points are constructed based on multiple predicted values ​​and reference period numbers, and linear regression analysis is performed on the multiple reference points to obtain the slope of the regression line.

[0104] The secondary judgment process also includes:

[0105] The obtained regression line slope is compared with the preset slope. If the obtained regression line slope is not less than the preset slope, it is determined that the current freezing temperature meets the requirements and does not need to be adjusted.

[0106] If the slope of the obtained regression line is less than the preset slope, it is determined that the current freezing temperature is close to the critical condition and the length of the control period needs to be reduced.

[0107] In this embodiment, a secondary judgment process is provided. The specific secondary judgment further subdivides and judges the special circumstances in the process of using the predicted value for suitability judgment. The basis of the secondary judgment is the changing trend of the predicted value. When the changing trend is upward, that is, the slope finally obtained is high, the change of the structural parts under the current freezing conditions is continuously improving, and there is no need to adjust the freezing temperature. On the contrary, the changing trend is downward. Since the various parameters are close to the critical conditions, the frequency of judging the suitability of adjusting the freezing temperature is increased by shortening the length of the control period, thereby maximizing the protection of the structural parts of the underground building.

[0108] A freezing pipe is arranged in the freezing hole. The freezing pipe is connected by welding with an inner liner pipe clamp and a thread. The tensile strength is not less than 75% of the mother pipe. The mother pipe is the freezing pipe used in the freezing station.

[0109] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A vertical freezing and thawing management system based on data prediction, comprising a monitoring module, an analysis and prediction module and a control module, characterized in that: The monitoring module includes a first monitoring unit and a second monitoring unit, wherein the first monitoring unit is arranged in a freezing area, wherein a plurality of freezing holes, temperature measuring holes and pressure relief holes are arranged in the freezing area, wherein the first monitoring unit is used to obtain the freezing temperature in the freezing holes, the first temperature of the temperature measuring holes, the freezing pressure of the pressure relief holes and the ambient temperature of the freezing area, and the second monitoring unit is arranged in an underground building outside the freezing area, and is used to obtain the third temperature and stress data of a plurality of structural members in the underground building that are affected by the low temperature; The analysis and prediction module normalizes the multiple parameters obtained by the first monitoring unit and obtains the state coefficient according to the normalized parameters. The analysis and prediction module builds a prediction model based on the multiple data obtained by the second monitoring unit and uses it to judge the suitability of the freezing temperature. The process of obtaining the state coefficients includes: The actual freezing temperature, the first temperatures of the plurality of temperature measuring holes, the ambient temperature and the freezing pressure of the pressure relief hole closest to the affected structural member are respectively standardized; Perform a weighted summation of the standardized first temperatures of a plurality of temperature measuring holes to obtain a temperature measurement coefficient; Performing a secondary weighted summation on the actual freezing temperature, ambient temperature, freezing pressure and the temperature measurement coefficient after the numerical standardization to obtain a state coefficient; The process of building a predictive model includes: Dividing the control period into a plurality of monitoring periods, and obtaining values ​​of the third temperature and stress data in the monitoring periods; Based on the values ​​of the third temperature and stress data, construct the function: in, For the constructed function, yes The two independent variables of is the difference between the third temperature at the start and end of the monitoring period, is the preset standard time value, is the difference between the first cumulative amount of stress of the structure obtained in the current monitoring period and the second cumulative amount of stress of the structure obtained in the previous monitoring period. It is the preset standard cumulative value; The control module is used to control the freezing temperature and set the control period. The freezing temperature in the first control period after startup is a preset value, and the freezing temperature is adjusted based on the applicability of the freezing temperature during the freezing process, the maintenance process, and the thawing process; The process of using the prediction model for the suitability of freezing temperature includes: In the state coefficient function graph corresponding to several monitoring periods, the integral value of the state coefficient in the monitoring period is obtained; Compare a number of integral values ​​with a preset reference value, and select a monitoring period in which the integral value is greater than the reference value; From the selected monitoring time periods whose integral values ​​are greater than the reference value, select the monitoring time period in which the difference between the third temperature at the end of the time period and the critical temperature is the smallest as the target time period; Substitute the corresponding data of the target period into the prediction model to obtain the prediction value, and make a suitability judgment based on the prediction value; The process of determining suitability based on predicted values ​​includes: Compare the predicted value with the preset discrimination interval; If the predicted value is on the left side of the discrimination interval, it is judged that the current freezing temperature is too low and needs to be adjusted; If the predicted value falls within the discrimination interval, a second judgment is required; If the predicted value is on the right side of the discrimination interval, it is judged that the current freezing temperature meets the requirements and does not need to be adjusted; The process of making a secondary judgment includes: Taking the target period as the benchmark, select several reference periods along the time axis; Substituting the corresponding data of the reference period into the prediction model to obtain multiple prediction values; Constructing multiple reference points based on multiple predicted values ​​and reference period numbers, and performing linear regression analysis on the multiple reference points to obtain the slope of the regression line; The secondary judgment process also includes: The obtained regression line slope is compared with the preset slope. If the obtained regression line slope is not less than the preset slope, it is determined that the current freezing temperature meets the requirements and does not need to be adjusted. If the slope of the obtained regression line is less than the preset slope, it is determined that the current freezing temperature is close to the critical condition and the length of the control period needs to be reduced.

2. A vertical freezing and thawing management system based on data prediction according to claim 1, characterized in that: The freezing area is provided with a number of freezing holes, temperature measuring holes, temperature control holes and pressure relief holes, including: The freezing holes include a freezing hole vertical to the ground and a horizontal freezing hole in the well for assisting freezing. The pressure relief holes are arranged in the well and are arranged horizontally in a plurality along the extending direction of the vertical freezing holes. The arrangement principle of the temperature measuring holes is to arrange them at the weak links of freezing, so as to measure the temperature of different parts of the freezing curtain.

3. A vertical freezing and thawing management system based on data prediction according to claim 1, characterized in that: A freezing pipe is arranged in the freezing hole, and the freezing pipe is connected by welding with an inner liner pipe clamp and a thread, and the tensile strength is not less than 75% of the mother pipe.

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