Ancient building safety monitoring system based on BIM technology

Abstract

The present invention discloses an ancient building safety monitoring system based on BIM technology. The present invention relates to the technical field of ancient buildings and solves the problem that safety monitoring is not comprehensive enough and cannot confirm the hidden dangers of safety nodes in ancient buildings in real time. The present invention deeply mines the historical data of abnormal nodes, determines the trend feature interval and compares it with the preset interval in the database, and can clearly judge the cause of the abnormality and display the corresponding abnormal signal; this provides strong support for the safety management and control of ancient buildings, and maintenance personnel can take targeted measures in time according to this information to reduce the risk of ancient buildings being damaged due to abnormal structural stress, greatly improving the efficiency and scientific nature of ancient building safety monitoring, and contributing to the long-term protection and sustainable development of ancient buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of ancient buildings, and in particular to an ancient building safety monitoring system based on BIM technology. Background Art

[0002] Ancient buildings carry rich historical and cultural information. Their spatial form not only reflects the architectural skills of the time, but also contains social, cultural and other connotations. Accurately identifying the spatial form of ancient buildings is of great significance for their protection, repair and historical and cultural research. Traditional methods for studying the spatial form of ancient buildings rely heavily on manual field measurement and recording, which is inefficient and its accuracy is greatly affected by human factors. With the development of computer vision technology, image-based recognition methods have provided a new approach to the study of the spatial form of ancient buildings. By collecting, processing and analyzing ancient building images, we can quickly and accurately obtain spatial form information of ancient buildings, providing strong support for the protection and research of ancient buildings.

[0003] The application with publication number CN111741094A discloses an ancient building environmental protection system based on wireless sensors; it includes a management terminal; a remote monitoring terminal and an early warning terminal connected to the management terminal via an external network; a remote management terminal; the management terminal uses a computer to receive data information of the ancient building monitored by the remote monitoring terminal through a wireless network transmission method, and stores the received data information in a database; each sensor detects and monitors the data of the wires in the working state, and the alarm is used to alarm and remind the maintenance personnel when the data exceeds the safety threshold, reminding the maintenance personnel to carry out maintenance; the positioning sensor is used to locate the number and position of the junction boxes installed on the ancient building, so that the maintenance personnel can repair the connection points of two adjacent wires connected by the junction boxes; the fire caused by the short-circuited wires can easily cause burns to the ancient buildings, thereby causing damage to the ancient building environment.

[0004] During the security monitoring process of ancient buildings, it is generally directly based on the collected relevant parameters to assess whether there are safety hazards in the corresponding building environment. However, the original safety hazard monitoring method of this type can only monitor the specific external environment, and the relevant data of the internal building nodes cannot be monitored. The comprehensiveness of its safety monitoring is insufficient, and it cannot confirm the safety node hazards in the ancient buildings in real time. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides an ancient building safety monitoring system based on BIM technology, which solves the problem of insufficient comprehensiveness of security monitoring and the inability to confirm the hidden dangers of safety nodes in ancient buildings in real time.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an ancient building safety monitoring system based on BIM technology, comprising:

[0007] The BIM model analysis end determines the axial force, bending moment, and shear force associated with the corresponding model node based on the associated model values ​​at the node position within the BIM model. The specific method is as follows:

[0008] Confirm the axial pressure N associated with the model node from the BIM model i , where i represents the different model nodes in this BIM model, and then confirm the cross-sectional area M associated with the corresponding model node load surface i ;

[0009] Using Z i =N i ÷M i Confirm the model node axial force Z i ;

[0010] The specific method of confirming the bending moment of the model node is: confirm the bending moment W transferred from the end of the beam associated with this model node to this node i , beam section moment of inertia G i , the distance L between the model node and the neutral axis of the beam i , using WJ i =(W i ×L i )÷G i Lock the bending moment WJ about this model node i ;

[0011] The specific method of confirming the shear force of the model node is: directly confirm the shear force O transferred to this node from the associated beam end in the BIM model. i , then confirm the cross-sectional area A of the corresponding associated beam end i , using JY i =O i ÷A i Confirm the shear force JY associated with the corresponding model node i ;

[0012] The stress data confirmation terminal confirms the comprehensive stress associated with the corresponding model node based on the axial force, bending moment and shear force associated with different model nodes in the BIM model. The specific method is as follows:

[0013] Based on the axial force Z associated with the corresponding model node i , bending moment WJ i and shear force JY i , based primarily on the associated axial force Z i , bending moment WJ i Lock the superposition force associated with the corresponding model node, where superposition force = Zi +WJ i , calibrate the confirmed superposition force as D i ;

[0014] Then use: Confirm the comprehensive stress, where ± represents two processing methods. Confirm two sets of comprehensive stresses. The comprehensive stress confirmed by the “+” value processing method in “±” is calibrated as the first comprehensive stress, and the comprehensive stress confirmed by the “-” value processing method in “±” is calibrated as the second comprehensive stress;

[0015] The node data verification end performs error checking on the comprehensive stress confirmed by the corresponding model node and the monitoring data associated with the strain gauge, identifies and confirms the difference between the comprehensive stress of the model node and the strain gauge detection data, and locks the abnormal node based on whether the identified difference meets the standard. The specific method is as follows:

[0016] The first comprehensive stress confirmed by the corresponding model node is calibrated as ZH1 i , the second comprehensive stress is calibrated as ZH2 i , and then calibrate the monitoring data associated with the strain gauge as YB i ;

[0017] Prioritize identification of the second comprehensive stress ZH2 i With YB i Is the difference between: |ZH2 i -YB i |≤Y1, where Y1 is the preset value:

[0018] If it meets the requirements, the first comprehensive stress is identified and calibrated as ZH1 i With YB i Is the difference between: |ZH1 i -YB i |≤Y1, if it meets the standard, it means that the numerical difference meets the standard. If it does not meet the standard, the node where the strain gauge is located is marked as an abnormal node;

[0019] If it does not meet the requirements, the node where the strain gauge is located will be directly marked as an abnormal node;

[0020] And transmit the marked abnormal nodes to the historical data analysis terminal;

[0021] The historical data analysis end extracts the historical data of the abnormal node, confirms the strain change trend of the abnormal node, and then verifies and analyzes the confirmed strain change trend to determine the trend characteristic interval. The determined trend characteristic interval is compared with the preset interval in the database. Based on the comparison result, the comparison signal is confirmed and displayed. The specific method is as follows:

[0022] Based on the current moment, a set of traceability cycles is confirmed, where the traceability cycle is a preset cycle. Based on the different strain gauge monitoring data associated with different moments in the traceability cycle, a strain gauge monitoring curve belonging to this abnormal node is generated.

[0023] From the generated strain gauge monitoring curve, the change trend between adjacent time nodes is identified, and the strain gauge monitoring data associated with the adjacent time nodes are calibrated as YB1 and YB2, where YB1 is the previous set of strain gauge monitoring data, and YB2 is the next set of strain gauge monitoring data. The change trend between adjacent time nodes is locked using the following formula: change trend = (YB2-YB1) ÷ time interval. The time interval is the time difference between adjacent time nodes, and the change trend between different adjacent time nodes is calibrated as BH. k , where k represents different adjacent time node curve segments;

[0024] From the confirmed changes in several groups of trends BH k In the trend feature interval, lock the trend feature interval: from several groups of change trends BH k A minimum value and a maximum value are selected, a set of variable intervals are confirmed based on the minimum value and the maximum value, and the endpoint values ​​of the variable intervals are adjusted. The adjusted endpoint values ​​of the intervals shall not exceed the confirmed minimum value and the maximum value. Several adjustment processes are performed, and each adjustment process is associated with a different variable interval.

[0025] For each different adjustment process, identify the value range Fq of the corresponding variable interval, where q represents the different variable intervals, and then confirm the change trend BH included in the corresponding variable interval k The total number Zq is used: Mq = Zq ÷ Fq to confirm the interval feature Mq associated with the corresponding variable interval. From the interval features Mq associated with different variable intervals, the variable interval associated with Mqmax is selected as the trend feature interval associated with this adjustment process.

[0026] Preferably, the historical data analysis terminal compares the determined trend feature interval with the preset interval in the database in the following specific manner:

[0027] According to the trend characteristic interval confirmed by the abnormal node, the trend characteristic interval is checked with the preset interval in the database, wherein the database stores three groups of preset intervals, the endpoint values ​​of the preset intervals are all preset values, and each group of preset intervals is different. The three groups of preset intervals are: axial force abnormal interval, bending moment abnormal interval and shear force abnormal interval;

[0028] Identify the overlapping range of this trend feature interval and its preset interval, and determine the comprehensive proportion value of the overlapping range in the trend feature interval and the preset interval, using: overlapping range ÷ trend feature interval = first proportion value, and then using: overlapping range ÷ preset interval = second proportion value, average the first proportion value and the second proportion value to confirm the comprehensive proportion value, and identify whether the comprehensive proportion value meets the following conditions: comprehensive proportion value ≥ 70%. If so, mark this preset interval as the signal output interval. If there is no preset interval that meets this condition, directly generate an error signal for display;

[0029] If the signal output interval is an abnormal axial force interval, an abnormal axial force signal is generated for display;

[0030] If the signal output interval is the bending moment force abnormal interval, a bending moment force abnormal signal is generated for display;

[0031] If the signal output interval is the shear force abnormality interval, a shear force abnormality signal is generated for display.

[0032] The present invention provides a safety monitoring system for ancient buildings based on BIM technology. Compared with the existing technology, it has the following advantages:

[0033] By performing error checking between the comprehensive stress of model nodes and the monitoring data of strain gauges, the present invention can quickly and accurately identify abnormal nodes through a rigorous judgment process. This close integration of theoretical models and actual monitoring data effectively ensures the accuracy of safety status assessments of ancient building nodes, promptly identifies potential safety hazards, and provides a clear direction for the maintenance and restoration of ancient buildings.

[0034] By deeply mining the historical data of abnormal nodes, determining the trend feature interval and comparing it with the preset interval in the database, we can clearly judge the cause of the abnormality and display the corresponding abnormal signal; this provides strong support for the safety management and control of ancient buildings. Maintenance personnel can take targeted measures in a timely manner based on this information to reduce the risk of damage to ancient buildings due to abnormal structural stress, greatly improving the efficiency and scientific nature of ancient building safety monitoring, and contributing to the long-term protection and sustainable development of ancient buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the principle framework of the present invention;

[0036] Figure 2 Schematic diagram of determining abnormal nodes in the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0038] First embodiment

[0039] See also Figure 1 , the present application provides an ancient building safety monitoring system based on BIM technology, including a BIM model analysis terminal, a stress data confirmation terminal, a node data verification terminal, a historical data analysis terminal and a database, wherein the BIM model analysis terminal, the stress data confirmation terminal, the node data verification terminal and the historical data analysis terminal are electrically connected from the output node to the input node in sequence, and the database is electrically connected to the input node of the historical data analysis terminal;

[0040] Among them, the BIM model analysis end has a preset BIM model of the relevant ancient buildings that need to be monitored for safety. The force analysis is confirmed for the preset BIM model. Based on the associated model values ​​at the node positions in the BIM model, the axial force, bending moment and shear force associated with the corresponding model node are confirmed, and the confirmed axial force, bending moment and shear force are transmitted to the stress data confirmation end. The specific method of confirming the axial force of the model node is as follows:

[0041] Confirm the axial pressure N associated with the model node from the BIM model i , where i represents the different model nodes in this BIM model, and then confirm the cross-sectional area M associated with the corresponding model node load surface i ;

[0042] Using Z i =N i ÷M i Confirm the model node axial force Z i The axial force of a single node is related to the pressure on the corresponding node and the corresponding cross-sectional area. Based on the pressure and the corresponding cross-sectional area, the axial force associated with the corresponding node can be locked. The pressure value of the corresponding node can be directly extracted from the corresponding BIM model.

[0043] The specific method of confirming the bending moment of the model node is as follows: confirm the bending moment W transferred from the end of the beam associated with this model node to this node. i , beam section moment of inertia G i , the distance L between the model node and the neutral axis of the beam i , using WJ i =(W i ×L i)÷G i Lock the bending moment WJ about this model node i ;

[0044] The specific method of confirming the shear force of the model node is: directly confirm the shear force O transferred to this node from the associated beam end in the BIM model. i , then confirm the cross-sectional area A of the corresponding associated beam end i , using JY i =O i ÷A i Confirm the shear force JY associated with the corresponding model node i ;

[0045] For different characteristic nodes in the model, the associated node data are directly extracted from the model, and then the relevant forces associated with the corresponding characteristic nodes are confirmed based on the extracted node data. The confirmed relevant forces are all forces associated with the standard values ​​in the model, which can serve as a better standard and facilitate subsequent actual comparison.

[0046] The stress data confirmation end confirms the comprehensive stress associated with the corresponding model node based on the axial force, bending moment, and shear force associated with different model nodes in the BIM model (this comprehensive stress is the specific force monitored by the subsequent strain gauge), and transmits the confirmed comprehensive stress to the node data verification end. The specific method for confirming the comprehensive stress is:

[0047] Based on the axial force Z associated with the corresponding model node i , bending moment WJ i and shear force JY i , based primarily on the associated axial force Z i , bending moment WJ i Lock the superposition force associated with the corresponding model node. The superposition of the normal stress generated by the axial force and the bending moment follows the linear superposition principle, that is, the forces can be added, where the superposition force = Z i +WJ i , calibrate the confirmed superposition force as D i ;

[0048] Then use: Confirm the comprehensive stress, where ± represents two processing methods. Confirm two sets of comprehensive stresses. The comprehensive stress confirmed by the "+" value processing method in "±" is calibrated as the first comprehensive stress (the maximum value, the first comprehensive stress determines the possibility of tensile failure of the material), and the comprehensive stress confirmed by the "-" value processing method in "±" is calibrated as the second comprehensive stress (the minimum value, the second comprehensive stress provides a numerical basis for compressive failure analysis). What is determined here is not an interval, but two sets of comprehensive stresses, each with a maximum and minimum value;

[0049] The comprehensive stress determined here needs to be transmitted to the node data verification end. The node data verification end performs a comprehensive analysis based on the stress data detected by the corresponding strain gauge and the specific comprehensive stress confirmed by this processing process to determine whether the corresponding comprehensive stress is consistent with the error in the numerical verification standard, so as to achieve better analysis and evaluation results and ensure the accuracy of the analysis and evaluation.

[0050] Among them, combined Figure 2 , the node data verification end performs error verification on the comprehensive stress confirmed by the corresponding model node and the monitoring data associated with the strain gauge, identifies and confirms the difference between the comprehensive stress of the model node and the strain gauge detection data, and confirms whether the identified difference meets the standard, and evaluates the abnormal node. The specific method of evaluation is as follows:

[0051] The first comprehensive stress confirmed by the corresponding model node is calibrated as ZH1 i , the second comprehensive stress is calibrated as ZH2 i , and then calibrate the monitoring data associated with the strain gauge as YB i ;

[0052] Prioritize identification of the second comprehensive stress ZH2 i With YB i Is the difference between: |ZH2 i -YB i |≤Y1, where Y1 is a preset value, and its specific value is determined by the operator based on experience:

[0053] If it meets the requirements, the first comprehensive stress is identified and calibrated as ZH1 i With YB i Is the difference between: |ZH1 i -YB i |≤Y1, if it meets the standard, it means that the numerical difference meets the standard. If it does not meet the standard, the node where the strain gauge is located is marked as an abnormal node;

[0054] If it does not meet the requirements, the node where the strain gauge is located will be directly marked as an abnormal node;

[0055] The marked abnormal nodes are then transmitted to the historical data analysis terminal.

[0056] Specifically, in the actual monitoring process, if there is a large difference between the monitored data and the standard data actually generated by the corresponding model, it means that there is a related numerical anomaly in the corresponding node. If there is no large difference, it means that there is no large related anomaly in the corresponding node and it is in a normal state. Therefore, after the corresponding numerical comparison results, it is possible to lock in whether the corresponding node is an abnormal node and conduct a comprehensive evaluation in the subsequent analysis process.

[0057] Second embodiment

[0058] The historical data analysis end extracts historical data of the calibrated abnormal node, confirms the strain change trend of the abnormal node from the extracted historical data, verifies and analyzes the confirmed strain change trend, determines the trend characteristic interval, compares the determined trend characteristic interval with the preset characteristic interval in the database, and confirms and displays the comparison signal based on the comparison result (determines the abnormal cause of the corresponding abnormal node and displays it in real time to facilitate safety management of ancient buildings). The specific method of determining the trend characteristic interval is as follows:

[0059] Based on the current moment, a set of traceability cycles is confirmed. The traceability cycle is a preset cycle, which is prepared in advance by relevant personnel based on experience. The traceability cycle is generally 30 days. Based on the different strain gauge monitoring data associated with different moments in the traceability cycle, a strain gauge monitoring curve belonging to this abnormal node is generated. The horizontal axis of this curve is the timeline, and its vertical axis is the strain gauge monitoring data (that is, stress). The initial endpoint of this monitoring curve is the initial moment of the traceability cycle, and its terminal endpoint is the current moment.

[0060] From the generated strain gauge monitoring curve, the change trend between adjacent time nodes is identified, and the strain gauge monitoring data associated with the adjacent time nodes are calibrated as YB1 and YB2, where YB1 is the previous set of strain gauge monitoring data, and YB2 is the next set of strain gauge monitoring data. The change trend between adjacent time nodes is locked using the following formula: change trend = (YB2-YB1) ÷ time interval. The time interval is the time difference between adjacent time nodes, and the change trend between different adjacent time nodes is calibrated as BH. k , where k represents different adjacent time node curve segments;

[0061] From the confirmed changes in several groups of trends BH k In the trend feature interval, lock the trend feature interval: from several groups of change trends BH k A minimum value and a maximum value are selected, a set of variable intervals are confirmed based on the minimum value and the maximum value, and the endpoint values ​​of the variable intervals are adjusted. The adjusted endpoint values ​​of the intervals shall not exceed the confirmed minimum value and the maximum value. Several adjustment processes are performed, and each adjustment process is associated with a different variable interval.

[0062] For each different adjustment process, identify the value range Fq of the corresponding variable interval, where q represents the different variable intervals, and then confirm the change trend BH included in the corresponding variable interval kThe total number Zq is determined by: Mq = Zq ÷ Fq to determine the interval feature Mq associated with the corresponding variable interval. From the interval features Mq associated with different variable intervals, the variable interval associated with Mqmax is selected as the trend feature interval associated with this adjustment process.

[0063] Specifically, the confirmed minimum and maximum values ​​are proposed to be 1 and 9, so the confirmed variable interval is [1, 9]. Based on this variable interval [1, 9], the subsequent variable intervals are confirmed, and the confirmed variable intervals can be adjusted to: [1, 8], [1, 7], ... [1, 2], [2, 9], [2, 8], etc. The variable intervals associated with each different interval range are different, so the interval characteristics associated with each different variable interval are also different. According to the corresponding interval range and the total number of associations, the specific confirmation of the interval characteristics is carried out. The larger the interval characteristics, the higher the density of the corresponding confirmed trend characteristic interval and the stronger its characteristics, so the corresponding trend characteristic interval can be locked.

[0064] The specific method of comparing the determined trend feature interval with the preset interval in the database is as follows:

[0065] According to the trend characteristic interval confirmed by the abnormal node, the trend characteristic interval is checked with the preset interval in the database, wherein the database stores three groups of preset intervals, the endpoint values ​​of the preset intervals are all preset values, and each group of preset intervals is different. The three groups of preset intervals are: axial force abnormal interval, bending moment abnormal interval and shear force abnormal interval;

[0066] Identify the overlapping range of this trend feature interval and its preset interval, and determine the comprehensive proportion value of the overlapping range in the trend feature interval and the preset interval, using: overlapping range ÷ trend feature interval = first proportion value, and then using: overlapping range ÷ preset interval = second proportion value, average the first proportion value and the second proportion value, confirm the comprehensive proportion value, and identify whether the comprehensive proportion value meets: comprehensive proportion value ≥ 70%. If so, mark this preset interval as the signal output interval. If there is no preset interval that meets this condition, directly generate an error signal for display. When the error signal exists, it means that there are major problems in its actual safety monitoring process, and its maintenance personnel need to perform maintenance in a timely manner. It is necessary to combine the actual situation of the building support and assess the relevant problems of its building support;

[0067] If the signal output interval is an abnormal axial force interval, an abnormal axial force signal is generated for display;

[0068] If the signal output interval is the bending moment force abnormal interval, a bending moment force abnormal signal is generated for display;

[0069] If the signal output interval is the shear force abnormality interval, a shear force abnormality signal is generated for display.

[0070] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0071] 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 ancient building safety monitoring system based on BIM technology is characterized by: include: The BIM model analysis end determines the axial force, bending moment, and shear force associated with the corresponding model node based on the associated model values ​​at the node position within the BIM model. The specific method is as follows: Confirm the axial pressure N associated with the model node from the BIM model i , where i represents the different model nodes in this BIM model, and then confirm the cross-sectional area M associated with the corresponding model node load surface i ; Using Z i =N i ÷M i Confirm the model node axial force Z i ; The specific method of confirming the bending moment of the model node is: confirm the bending moment W transferred from the end of the beam associated with this model node to this node i , beam section moment of inertia G i , the distance L between the model node and the neutral axis of the beam i , using WJ i =(W i ×L i )÷G i Lock the bending moment WJ about this model node i ; The specific method of confirming the shear force of the model node is: directly confirm the shear force O transferred to this node from the associated beam end in the BIM model. i , then confirm the cross-sectional area A of the corresponding associated beam end i , using JY i =O i ÷A i Confirm the shear force JY associated with the corresponding model node i ; The stress data confirmation terminal confirms the comprehensive stress associated with the corresponding model node based on the axial force, bending moment and shear force associated with different model nodes in the BIM model. The specific method is as follows: Based on the axial force Z associated with the corresponding model node i , bending moment WJ i and shear force JY i , based primarily on the associated axial force Z i , bending moment WJ i Lock the superposition force associated with the corresponding model node, where superposition force = Z i +WJ i , calibrate the confirmed superposition force as D i ; Then use: Confirm the comprehensive stress, where ± represents two processing methods. Confirm two sets of comprehensive stresses. The comprehensive stress confirmed by the "+" value processing method in "±" is calibrated as the first comprehensive stress, and the comprehensive stress confirmed by the "-" value processing method in "±" is calibrated as the second comprehensive stress; The node data verification end performs error checking on the comprehensive stress confirmed by the corresponding model node and the monitoring data associated with the strain gauge, identifies and confirms the difference between the comprehensive stress of the model node and the strain gauge detection data, and locks the abnormal node based on whether the identified difference meets the standard. The specific method is as follows: The first comprehensive stress confirmed by the corresponding model node is calibrated as ZH1 i , the second comprehensive stress is calibrated as ZH2 i , and then calibrate the monitoring data associated with the strain gauge as YB i ; Prioritize identification of the second comprehensive stress ZH2 i With YB i Is the difference between: |ZH2 i -YB i |≤Y1, where Y1 is the preset value: If it meets the requirements, the first comprehensive stress is identified and calibrated as ZH1 i With YB i Is the difference between: |ZH1 i -YB i |≤Y1, if it meets the standard, it means that the numerical difference meets the standard. If it does not meet the standard, the node where the strain gauge is located is marked as an abnormal node; If it does not meet the requirements, the node where the strain gauge is located will be directly marked as an abnormal node; And transmit the marked abnormal nodes to the historical data analysis terminal; On the historical data analysis side, the historical data of the abnormal node is extracted to confirm the strain change trend of the abnormal node. The confirmed strain change trend is then verified and analyzed to determine the trend characteristic interval. The determined trend characteristic interval is compared with the preset interval in the database. Based on the comparison result, the comparison signal is confirmed and displayed.

2. The ancient building safety monitoring system based on BIM technology according to claim 1 is characterized in that: The specific method of determining the trend feature interval at the historical data analysis end is: Based on the current moment, a set of traceability cycles is confirmed, where the traceability cycle is a preset cycle. Based on the different strain gauge monitoring data associated with different moments in the traceability cycle, a strain gauge monitoring curve belonging to this abnormal node is generated. From the generated strain gauge monitoring curve, the change trend between adjacent time nodes is identified, and the strain gauge monitoring data associated with the adjacent time nodes are calibrated as YB1 and YB2, where YB1 is the previous set of strain gauge monitoring data, and YB2 is the next set of strain gauge monitoring data. The change trend between adjacent time nodes is locked using the following formula: change trend = (YB2-YB1) ÷ time interval. The time interval is the time difference between adjacent time nodes, and the change trend between different adjacent time nodes is calibrated as BH. k , where k represents different adjacent time node curve segments; From the confirmed changes in several groups of trends BH k In the trend feature interval, lock the trend feature interval: from several groups of change trends BH k A minimum value and a maximum value are selected, a set of variable intervals are confirmed based on the minimum value and the maximum value, and the endpoint values ​​of the variable intervals are adjusted. The adjusted endpoint values ​​of the intervals shall not exceed the confirmed minimum value and the maximum value. Several adjustment processes are performed, and each adjustment process is associated with a different variable interval. For each different adjustment process, identify the value range Fq of the corresponding variable interval, where q represents the different variable intervals, and then confirm the change trend BH included in the corresponding variable interval k The total number Zq is used: Mq = Zq ÷ Fq to confirm the interval feature Mq associated with the corresponding variable interval. From the interval features Mq associated with different variable intervals, the variable interval associated with Mqmax is selected as the trend feature interval associated with this adjustment process.

3. The ancient building safety monitoring system based on BIM technology according to claim 2 is characterized in that: The specific method of comparing the determined trend feature interval with the preset interval in the database by the historical data analysis terminal is as follows: According to the trend characteristic interval confirmed by the abnormal node, the trend characteristic interval is checked with the preset interval in the database, wherein the database stores three groups of preset intervals, the endpoint values ​​of the preset intervals are all preset values, and each group of preset intervals is different. The three groups of preset intervals are: axial force abnormal interval, bending moment abnormal interval and shear force abnormal interval; Identify the overlapping range of this trend feature interval and its preset interval, and determine the comprehensive proportion value of the overlapping range in the trend feature interval and the preset interval, using: overlapping range ÷ trend feature interval = first proportion value, and then using: overlapping range ÷ preset interval = second proportion value, average the first proportion value and the second proportion value, confirm the comprehensive proportion value, and identify whether the comprehensive proportion value meets: comprehensive proportion value ≥ 70%. If so, this preset interval is calibrated as the signal output interval. If there is no preset interval that meets this condition, an error signal is directly generated for display.

4. The ancient building safety monitoring system based on BIM technology according to claim 3 is characterized in that: If the signal output interval is an abnormal axial force interval, an abnormal axial force signal is generated for display; If the signal output interval is a bending moment force abnormal interval, generating a bending moment force abnormal signal for display; If the signal output interval is a shear force abnormality interval, a shear force abnormality signal is generated for display.

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