House safety risk intelligent monitoring system and method based on Beidou and GIS platform

Abstract

The invention relates to the technical field of electrical digital data processing, and particularly discloses a house safety risk intelligent monitoring system and method based on a Beidou and GIS platform, and the system is provided with an original deformation time sequence statistics module, an absolute deformation time sequence statistics module, a house safety early warning judgment module and a regional risk surface visualization module. Obtaining an original deformation time sequence and an absolute deformation time sequence, performing limited item decomposition on the absolute deformation time sequence, extracting characteristics such as net deformation, a change rate, an interlayer drift ratio and a crack width growth rate, and performing event detection and early warning judgment in combination with the original deformation time sequence and a GIS grid factor; and finally, carrying out quality weighted interpolation by taking the extracted features as input, generating a regionalized risk surface, superposing the regionalized risk surface to a preset GIS base map, and automatically forming a geofence to realize visual delineation, wherein the regionalized risk surface and the geofence intuitively show where changes, so that linkage sealing control, work order distribution and path guidance are facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric digital data processing, in particular to a house safety risk intelligent monitoring system and method based on Beidou and GIS platform. BACKGROUND

[0002] The existing house safety risk intelligent monitoring usually adopts an "end-edge-cloud" architecture: multi-source sensors are arranged at key components and hidden danger points, connected to edge gateways through LoRa / NB-IoT / 4G, etc. for clock synchronization, noise removal / abnormal point elimination and rapid alarm judgment, and then uploaded to the cloud for feature extraction and fusion, combined with specification threshold and data-driven method to evaluate structure, environment and personal safety risk, trigger graded alarm and work order closed loop; at the same time, BIM / digital twin visualization trend and spatial distribution are used to reduce false positives and false negatives and support long-term online monitoring.

[0003] For example, the Chinese invention patent with publication number CN118780622A discloses a house safety monitoring system and method based on sensor data fusion, relating to the technical field of house monitoring and control. By collecting house sensor fusion monitoring data, including house stress and strain data, relative settlement data, inclination data and crack data; the house sensor fusion monitoring data is preprocessed to obtain usable monitoring data, and the usable monitoring data is divided into a training set and a test set; the usable monitoring data is analyzed and risk is determined to generate an abnormal alarm signal; a house safety level prediction model is established based on the test set to generate a house safety level; the corresponding processing is performed based on the abnormal alarm signal and the result of the house safety level.

[0004] For example, the Chinese invention patent with publication number CN120318020A discloses a house structure safety Internet of Things remote monitoring system based on multi-source data fusion, relating to the technical field of building structure monitoring. The target house is divided into different structure modules, and data acquisition terminals are arranged in the structure modules to collect multi-dimensional data information. The obtained multi-dimensional data information is used to analyze and determine whether the position of each data acquisition terminal is a structure risk point. If it is a structure risk point, based on the correlation between the abnormal data items that cause the structure risk point and other parameter items, and the correlation between the data acquisition terminal at the position of the structure risk point and other data acquisition terminals, a corresponding simulation stress cloud map is generated using the simulation value corresponding to the abnormal data items, thereby monitoring the possible risks of the house structure. At the same time, according to the structure risk point that has already appeared, the other positions that may be affected by the structure risk point are evaluated.

[0005] In combination with the above technical solutions, it is found that the existing house safety risk monitoring mostly relies on various types of field sensors for relative measurement and determination in a local coordinate, and the output is mainly threshold alarm and safety level, and the positioning relies on single-point positioning instruments; however, the sensor coverage range is generally limited (point / line, difficult to form a planar continuous perception), and is easily affected by multipath, water ingress, frosting, temperature drift and chain breakage in extreme weather such as strong wind, heavy rain, low temperature icing and high temperature exposure, resulting in data loss or noise increase; in addition, there is a lack of regional spatial inference and unified geodetic reference, and the monitoring results still remain at the level of the engineering local coordinate system, making it difficult to directly see "where is changing and how fast it is changing", and different houses or cross-season deformation are also difficult to directly compare and synchronize, thereby limiting the granularity of the linkage decision of house safety monitoring. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a house safety risk intelligent monitoring system and method based on Beidou and GIS platform, which can effectively solve the problems involved in the above background art.

[0007] To achieve the above object, the present application is realized by the following technical solutions: the first aspect of the present application provides a house safety risk intelligent monitoring system based on Beidou and GIS platform, comprising: an original deformation time series statistical module, configured to construct a reference network according to a preset reference station layout coordinate and a layout coordinate of each building control point of the house in a house safety monitoring area, extract the relative coordinates of each building control point of the house with respect to the reference station, and perform epoch difference statistics on the absolute coordinates to form the original deformation time series of each building control point of the house; an absolute deformation time series statistical module, configured to monitor the coordinates of each building control point of the house based on a Beidou monitoring end, obtain the absolute coordinates of each building control point of the house, and perform epoch difference statistics to form the absolute deformation time series of each building control point of the house; a house safety early warning determination module, configured to decompose the absolute deformation time series by a limited term, extract features from the decomposed absolute deformation time series, obtain the extracted features of the absolute deformation time series, and detect events of the house safety in combination with the original deformation time series and GIS grid factors to determine whether to issue a house safety risk early warning; a regionalized risk surface visualization module, configured to generate a regionalized risk surface based on the extracted features of the absolute deformation time series, visualize the regionalized risk surface by forming a geographic fence on a preset GIS map of the house safety monitoring area, and complete the house safety risk intelligent monitoring.

[0008] The second aspect of the present application provides a housing safety risk intelligent monitoring method based on Beidou and GIS platform, comprising: constructing a reference network according to preset reference station layout coordinates and building control point layout coordinates of each building in a housing safety monitoring area, extracting relative coordinates of each building control point relative to the reference station for epoch difference statistics to form an original deformation time sequence of each building control point; monitoring the coordinates of each building control point based on a Beidou monitoring terminal to obtain absolute coordinates of each building control point, performing epoch difference statistics on the absolute coordinates to form an absolute deformation time sequence of each building control point; decomposing the absolute deformation time sequence into limited items, and extracting features from the decomposed absolute deformation time sequence to obtain extracted features of the absolute deformation time sequence, combining the original deformation time sequence and GIS grid factors to detect events for housing safety, and determining whether to issue a housing safety risk warning; generating a regionalized risk surface based on the extracted features of the absolute deformation time sequence, and visualizing the regionalized risk surface by forming a geographic fence on a preset GIS map of the housing safety monitoring area to complete intelligent monitoring of housing safety risk.

[0009] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects: (1) The present application provides a housing safety risk intelligent monitoring system and method based on Beidou and GIS platform, a reference network is constructed according to preset reference station and building control point coordinates, relative coordinates of the control points relative to the reference station are extracted and epoch difference is performed to form an original deformation time sequence, the reference network provides unified time and space reference and multiple baseline redundancy to ensure the reliability and traceability of the relative quantity; meanwhile, absolute coordinates of the control points are obtained by the Beidou monitoring terminal and epoch difference is performed to form an absolute deformation time sequence, the absolute deformation can be compared across floors and seasons under the unified reference to support spatial inference; the absolute deformation time sequence is decomposed into limited items and features such as net deformation, change rate, interlayer drift ratio, and crack width growth rate are extracted, combined with the original deformation time sequence and GIS grid factors to carry out event detection and warning determination; finally, the extracted features are input for quality weighted interpolation to generate a regionalized risk surface, which is superimposed on a preset GIS base map and automatically forms a geographic fence to realize visual delineation, and the regionalized risk surface and the geographic fence intuitively show "where is changing and how fast it is changing", which facilitates linkage control, work order distribution, and path guidance.

[0010] (2) The present application determines the housing safety risk score, normalizes and weights the net deformation, growth rate, interlayer drift ratio, and crack growth rate according to the quality and scenario factors to form an interpretable quantitative score, and unifies the determination caliber of different indicators and different houses; the score can be smoothly tracked over time and set to enter / exit thresholds, which improves the advance amount and suppresses jitter, facilitates hierarchical response and accurate resource investment according to the score, and can be played back and versioned to support auditing.

[0011] (3) In the embodiment of the present application, the original deformation is used for mass inspection and gap segmentation, the absolute deformation is used for cross-target alignment and spatial expression, and the GIS grid factor is used for scenario correction and spatial constraint. These parameters are repeatedly used in the links of calculation-decomposition-detection-scoring-interpolation to form a data closed loop. Thus, the anti-missing measurement capability and robustness are improved without additional sensors, the operation and maintenance cost is reduced, and the consistency and comparability between different modules are maintained.

[0012] (4) The embodiment of the present application upgrades the point monitoring of the traditional local coordinate combined with threshold alarm to the regional risk surface expression of the unified reference combined with Beidou timing and GIS. The limited term decomposition, non-cross-gap derivation and scenario threshold are introduced to make the judgment more stable and less false alarm. Through the multi-baseline redundancy of the reference network and the linkage of the geographic fence, higher invulnerability, more intuitive visualization and more efficient disposal closed loop are realized, and the early warning timeliness and decision granularity are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0013] The present application is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application. For ordinary skilled in the art, other drawings can be obtained without creative labor on the premise of the following drawings.

[0014] Figure 1 It is a schematic diagram of the system module of the present application.

[0015] Figure 2 It is a schematic diagram of the method step flow of the present application.

[0016] Figure 3 It is a real-time situation overview interface of the housing safety monitoring platform.

[0017] Figure 4 It is a building body overview interface of the housing safety monitoring platform. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application.

[0019] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. In fact, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0020] In the embodiments of the present application, "image" and "picture" can be used interchangeably, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "relevant" can be used interchangeably, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0021] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail with reference to the drawings and specific embodiments.

[0023] In the application of Beidou and GIS platform in house safety risk intelligent monitoring, Beidou usually provides accurate position and unified time, GIS provides spatial organization and space-time analysis; the combination of the two turns the measured risk into actionable decision on the map, and makes the house safety monitoring upgrade from point data to visual, analyzable and linkable space-time risk management system.

[0024] Referring to Figure 1 The first aspect of the present application provides a house safety risk intelligent monitoring system based on Beidou and GIS platform, comprising: an original deformation time sequence statistical module, an absolute deformation time sequence statistical module, a house safety early warning judgment module, a regionalized risk surface visualization module and a risk monitoring management library. The risk monitoring management library is used for storing preset values of various parameters.

[0025] The preset relationship acquisition process involved in the present embodiment includes but is not limited to predefinition acquisition, matching, mapping, etc. Taking the mapping relationship between the weight parameter corresponding to the maximum span and the predefined maximum span as an example, first, the maximum span is denoted as L 区 , and then the reference span L 0,区 that meets the accuracy and communication requirements under the same type of project is determined (according to the target of reference station density, baseline length, interpolation error, communication delay, etc.), forming the span ratio r=L 区 / L 0,区Then the monotonic and limited weight curve is constructed by three-source calibration: ① benchmark network simulation (the growth law of geometric accuracy, interpolation error and time delay with r in different regional span); ② historical project statistics (the change of false alarm / misreport and disposal timeliness with r in large span area); ③ expert rules (the risk weight should be increased by several times). According to the above, a plurality of anchor points are set and piecewise linear or monotonic spline fitting is performed, the mapping set w(r) in the library is obtained by constraining that r does not decrease, smoothly transitions and the maximum value is limited (for example: r is less than or equal to 1, which is mapped to w=1, r is equal to 1.5, which is mapped to w=1.2, r is equal to 2, which is mapped to w=1.5, r is greater than or equal to 3, which is mapped to w=1.8; the numerical value is recalibrated according to the project). During operation, only the r of the current area needs to be calculated, and the weight parameter corresponding to the maximum span of the area is obtained by looking up / interpolating in the mapping set, which is used for benchmark network construction.

[0026] The original deformation time sequence statistics module is connected with the absolute deformation time sequence statistics module, the absolute deformation time sequence statistics module is connected with the house safety early warning judgment module, the house safety early warning judgment module is connected with the regionalized risk surface visualization module, and the original deformation time sequence statistics module, the absolute deformation time sequence statistics module, the house safety early warning judgment module and the regionalized risk surface visualization module are all connected with the risk monitoring management library.

[0027] Before on-site monitoring of the monitoring area, the benchmark unification of the monitoring area is completed: the national geodetic coordinate and the national height datum are selected as the only reference, the parallel reference station and the building control point are laid out, the longitude, latitude and height output by each monitoring device are unified and converted into plane coordinates under the selected projection (such as Gauss-Kruger), and the ellipsoidal height is converted into normal height by the geodetic datum model; at the same time, the horizontal and vertical integrated constraints are completed through the leveling and network adjustment, and the regional unified coordinate and height frame starting from the reference epoch point are formed. After completion, the building contour, terrain, road, pipeline, reference station and control point and other elements are collected, the monitoring area basic GIS layer and metadata (including benchmark, epoch point, precision index, quality identification) are generated, the plane and height precision requirements (such as plane centimeter level, vertical millimeter level) are clarified, and the coordinate service and layer publishing are established, so that all subsequent displacement, drift ratio and risk surface calculation and visualization are directly compared, superimposed and traced under the same benchmark.

[0028] The embodiment is aimed at monitoring multiple houses in a monitoring area, and selects one of them as a demonstration object to carry out intelligent safety risk monitoring: under unified coordinates and national elevation datum, relying on the built reference station, building control points are arranged on the roof and key components of the house, continuous collection is started and unification of time, coordinates and elevation is completed; quality inspection and elimination are performed on the epoch point data, original deformation sequence is established, and then core indexes such as net deformation, change rate and interlayer drift ratio are obtained; threshold self-adaptation and risk scoring are performed in combination with scene factors such as wind, rain and earthquake, four levels of information, early warning, alarm and emergency are automatically judged according to the information, and fences and disposal suggestions are generated on the map, while work orders, reviews and report export are linked to form a closed loop; the above process and parameters can be copied to other houses in the region in the same way to realize parallel monitoring of multiple houses and regional risk surface expression, without constituting a limitation on the protection range.

[0029] The specific safety risk monitoring process in the embodiment of the application is realized based on a housing safety monitoring platform, as shown in Figure 3 Figure 3 It is a real-time situation overview interface of the housing safety monitoring platform, and is a situation awareness and dispatching for the whole region command. It summarizes online rate, abnormal point number, real-time risk score and system state, superimposes environmental background such as wind, rain and earthquake and reference station health, helps to quickly grasp the overall risk level and reference network reliability, and makes hierarchical response, resource allocation and threshold strategy adjustment according to the above-mentioned information - solves the problem of “where is the overall risk, and where to save first”.

[0030] The original deformation time sequence statistical module is used for reference network construction in the housing safety monitoring area according to the preset reference station arrangement coordinates and the arrangement coordinates of each building control point of the house, extracting the relative coordinates of each building control point of the house relative to the reference station, epoch difference statistical of the absolute coordinates, forming the original deformation time sequence of each building control point of the house.

[0031] The above-mentioned reference station refers to one or a few fixed Beidou reference points arranged on an open and stable foundation, and the three-dimensional coordinates of the reference station have been measured with high precision under the national geodetic coordinate / elevation datum and have been kept still for a long time; it continuously collects satellite observations and outputs differential information required for ephemeris / clock correction and baseline solution, and can also provide unified time service and coordinate conversion parameters. The role is to “nail” the whole monitoring area on a unified and traceable time and space datum: to provide reference and correction for RTK / PPP of each building survey point, to ensure the comparability of cross-building / seasonal deformation, to monitor the stability to identify systematic drift, and to serve as a “anchor point” for data and time in extreme working conditions.

[0032] ​The above-mentioned building control points refer to Beidou measuring points or coordinate-calibrated measuring marks arranged at key positions of each building (such as the roof, parapet, core tube, and foundation edge), and, if necessary, adjacent to the inclinometer, static level, and crack meter, and jointly calibrated; the coordinates are unified to the national reference through the reference station / PPP, and continuously updated in time series. As the “deformation anchor point and fusion hub” of each building: directly outputting the absolute displacement / settlement / tilt sequence of the building, bearing the fusion and calibration with relative sensors, supporting spatial inference and risk surface generation within and between floors, and accurately positioning the alarm and work order to the floor / member, realizing the linkage decision of single and regional.

[0033] Specifically, the reference station layout coordinates and the layout coordinates of the building control points are analyzed as follows: The maximum span of the building safety monitoring area and the openness of the building safety monitoring area are obtained, and the construction characteristic value of the building safety monitoring area is obtained by coupling. Specifically, the maximum span of the building safety monitoring area is multiplied by the weight parameter corresponding to the predefined maximum span to obtain the first component of the construction characteristic of the building safety monitoring area, the openness of the building safety monitoring area is multiplied by the weight parameter corresponding to the predefined openness to obtain the second component of the construction characteristic of the building safety monitoring area, and the first component of the construction characteristic of the building safety monitoring area is added to the second component of the construction characteristic of the building safety monitoring area to obtain the construction characteristic value of the building safety monitoring area. It needs to be explained that the above coupling process is based on the normalized results of the maximum span of the building safety monitoring area and the openness of the building safety monitoring area. The weight parameters are extracted from the risk monitoring management library.

[0034] The maximum span can be extracted from the monitoring records of the building safety monitoring area. The openness represents the visibility of the sky or reflects the surrounding obstruction degree of the building safety monitoring area. The larger the value, the more open, and the smaller the surrounding obstruction degree. By shooting an fisheye panorama upward at several points in the building safety monitoring area. Segment the sky / obstacle pixels of several fisheye panoramas, and the average ratio is the openness.

[0035] The construction characteristic value of the building safety monitoring area is matched with the reference station layout number corresponding to each construction characteristic value interval predefined in the risk monitoring management library to determine the interval to which the construction characteristic value of the building safety monitoring area belongs, and the reference station layout number corresponding to the interval is obtained. The reference station layout number is recorded as the reference station layout adaptation number.

[0036] Wherein the reference station layout adaptation number is monotonically increasing with the construction characteristic value, that is, the larger the construction characteristic value, the more the reference station layout adaptation number. The larger the construction characteristic value indicates that it is more difficult to maintain the solution quality under the constraints of limited baseline length, coverage redundancy, geometric accuracy and anti-destroying partition (cross-partition layout), so a higher density and more number of reference stations are needed to ensure the stability of the space-time reference, controlled interpolation error and link redundancy.

[0037] The maximum elevation value of the house is obtained, wherein the elevation value can be extracted in the basic information record of the house, and the construction influence quantity of the house is obtained by weighting coupling with the construction characteristic value of the house safety monitoring area. The specific coupling is to multiply the maximum elevation value of the house by the weight parameter corresponding to the predefined maximum elevation value in the risk monitoring management library to obtain the first component of the construction influence of the house, multiply the construction characteristic value of the house safety monitoring area by the weight parameter corresponding to the predefined construction characteristic value to obtain the second component of the construction influence of the house, and add the first component of the construction influence of the house and the second component of the construction influence of the house to obtain the construction influence quantity of the house.

[0038] The construction influence quantity of the house is matched with the building control point layout number corresponding to each construction influence quantity interval to determine the specific interval of the construction influence quantity of the house, and the building control point layout number corresponding to the interval is obtained. The obtained building control point layout number is recorded as the building control point layout adaptation number.

[0039] Wherein the building control point layout adaptation number is monotonically increasing with the construction influence quantity of the house, that is, the larger the construction influence quantity of the house, the more the building control point layout adaptation number. The larger the construction influence quantity, the more irregular the house in plane or elevation, the more complex or key components (large cantilever, conversion layer, concave-convex and eccentricity, weak layer, expansion joint separation, multi-tower connection, foundation difference, etc.), and the more likely the deformation field to be non-uniform and contain torsion. In order to reliably identify the differential settlement, relative displacement between layers and torsion component and cross-check, it is necessary to improve the spatial resolution and redundancy of observation, so the number of building control points should be monotonically increased with the construction influence quantity.

[0040] The reference station layout adaptation number, the building control point layout adaptation number and the preset layout constraint set are input into the mixed integer linear programming algorithm, and the reference station layout coordinates and the layout coordinates of each building control point of the house are output.

[0041] The number of reference stations, the number of building control points, and a preset layout constraint set are taken as inputs to construct a mixed integer linear programming, and the layout coordinates of the reference stations and the building control points are automatically selected and output from the preselected candidate point set in the unified coordinate system. The constraint set can include the following: quantity constraint (total number of reference stations, number of control points per building, not less than three points, and not collinear), coverage and redundancy constraint (each control point is served by at least two reference stations, and the monitoring grid is covered by at least a certain number of reference stations), baseline constraint (the distance from the reference station to the control point falls within a given interval and has a clear view), geometric and signal quality constraint (sky openness is not less than a threshold, carrier-to-noise ratio and geometric dilution of precision meet the lower / upper limit requirements), minimum spacing and azimuth distribution constraint (the spacing between points on the same roof is not less than a specified value, and the control points are distributed in at least three azimuth sectors), partitioned anti-destroying constraint (the reference stations are distributed in different power supply or communication partitions, and each partition has at least a specified number of reference stations), construction and safety constraint (avoiding the forbidden buffer zone, and prioritizing the core tube, four corners, and transfer layer), etc. The coordinate results are directly given after solving.

[0042] For example, the east coordinates, north coordinates, and normal heights of the reference stations and the control points are output.

[0043] Reference station 1: east coordinate 345120.0 meters, north coordinate 2975430.0 meters, normal height 42.38 meters.

[0044] Building A: control point 1: east coordinate 345185.6 meters, north coordinate 2975488.2 meters, normal height 39.92 meters.

[0045] The reference station layout coordinates and the building control point layout coordinates are used to construct a reference network.

[0046] In this embodiment, the reference network is constructed to form a time-space integrated and redundant monitoring framework under the unified geodetic coordinate and national height datum. On the one hand, it continuously provides high-precision correction and unified time service for all measurement points, significantly improves the precision and comparability of absolute displacement, interlayer drift ratio, etc. (directly aligned across floors and seasons), and reduces false positives / misreports caused by multipath, obstruction, and instrument drift. On the other hand, it realizes anti-destroying and self-diagnosis through network redundancy and quality automatic checking (baseline closure error, coordinate stability, and reference station mutual monitoring), maintains stable calculation and traceable results even in the presence of single station anomalies. At the same time, it provides a uniform and reliable control framework for GIS risk surface interpolation, making it more intuitive and reliable to determine where and how fast changes are occurring, and supporting construction lofting, review, and emergency communication (short message) linkage, ultimately achieving a comprehensive effect of shortened deployment period, reduced operation and maintenance cost, more timely early warning, and finer decision granularity.

[0047] The above reference network construction is specifically for the overall framework construction of the monitored building, as follows: Figure 4As shown, Figure 4 The building body overview interface belongs to the housing intelligent monitoring platform, and is used for fine diagnosis and treatment of a single building. The three-dimensional distribution points and the surrounding environment are intuitively presented, and the near key feature curve (for example, the net deformation) is used to locate which building and point is changing, to judge whether it is continuous or sudden, and to initiate review, adjust the fence or export a single building report according to the above, so as to solve the problem of "how this building is and how fast it changes".

[0048] Further, the original deformation time sequence of each building control point of the house is formed, and the specific analysis process is as follows: At each epoch point, the relative coordinates of each building control point of the house relative to the reference station are extracted, and the relative coordinates of each building control point of the house at each epoch point are obtained and recorded as the relative coordinates of each building control point of the house at each epoch point, wherein the epoch point represents the sampling time point or sampling time of the relative coordinates.

[0049] The epoch point of the first extracted relative coordinates is recorded as the reference epoch point, and the relative coordinates of each building control point of the house at the current epoch point are differentially processed with the relative coordinates of the corresponding building control point at the reference epoch point, to obtain the relative coordinate deviation of each building control point at the epoch point. The relative coordinate difference between the current epoch point and the reference epoch point is represented as the relative coordinate difference.

[0050] The relative coordinate deviations of each building control point of the house at the epoch difference point are counted and sorted according to the relative epoch point sequence to obtain the original deformation time sequence of each building control point of the house.

[0051] In this embodiment, the original deformation time sequence is obtained, which can be used as "ground data" for full-link judgment. It is not only used for quality examination and abnormal identification (cycle slip, false fixing, missing measurement, geometric degradation, multipath / watering, etc.), timely labeling and removing poor epoch points, but also provides reliable weight and covariance prior for multi-source fusion, reduces the pull of poor observations on the result, significantly reduces false positives / misses; it can also support baseline / seasonal / temperature term stripping to obtain stable net deformation, slope and acceleration, and improve the sensitivity and reliability of trend and suddenness judgment; and in extreme working conditions or algorithm anomalies, it can be used as an emergency bottom and review basis to ensure that the whole monitoring-early warning-treatment chain is interpretable, replayable and verifiable.

[0052] The absolute deformation time sequence statistical module is used for coordinate monitoring of each building control point of the house based on the Beidou monitoring end to obtain the absolute coordinates of each building control point of the house for epoch difference statistics to form the absolute deformation time sequence of each building control point of the house.

[0053] Specifically, the absolute deformation time sequence of each building control point of the house is formed, and the specific analysis process is as follows: The Beidou monitoring end monitors the real-time absolute coordinates of each building control point of the house to obtain the absolute coordinates of each building control point of the house at each epoch point.

[0054] The absolute coordinates of each building control point of the house at each epoch are optimized, specifically: The GIS platform receives the absolute coordinate monitoring data transmitted by the Beidou monitoring terminal in real time, extracts the transmission carrier-to-noise ratio of the Beidou monitoring terminal at each epoch and the phase cycle slip of the Beidou monitoring terminal at each epoch, and combines the quality evaluation influence element of the monitoring openness of the house safety monitoring area to obtain the coordinate quality evaluation parameter at each epoch. The transmission carrier-to-noise ratio and the phase cycle slip can be extracted in the receiving record of the GIS platform.

[0055] The specific coupling process is: The monitoring openness of the house safety monitoring area is matched with the quality evaluation influence element corresponding to each monitoring openness interval predefined in the risk monitoring management library to determine the specific interval of the monitoring openness of the house safety monitoring area, and the quality evaluation influence element corresponding to the interval is obtained.

[0056] The quality evaluation influence element monotonically increases with the openness, and the greater the openness, the greater the quality evaluation influence element obtained. High openness means more visible satellites, higher carrier-to-noise ratio, better geometric accuracy, fewer cycle slips and multipath, and more stable timing, thereby improving the quality of coordinate and deformation calculation.

[0057] The transmission carrier-to-noise ratio of the Beidou monitoring terminal at each epoch and the phase cycle slip of the Beidou monitoring terminal at each epoch are normalized, and the normalized results are weighted and aggregated with the quality evaluation influence element to obtain the coordinate quality evaluation parameter at each epoch, specifically:

[0058] In the formula, QAy is the coordinate quality evaluation parameter at the yth epoch, y is the number of each epoch, Y is the total amount of epochs, CN0 y is the transmission carrier-to-noise ratio of the Beidou monitoring terminal at the yth epoch, GF y is the phase cycle slip of the Beidou monitoring terminal at the yth epoch, Q is the quality evaluation influence element, d1 is the weight factor corresponding to the transmission carrier-to-noise ratio predefined in the risk monitoring management library, and d2 is the weight factor corresponding to the phase cycle slip predefined in the risk monitoring management library.

[0059] It needs to be explained that the above transmission carrier-to-noise ratio is a core index for measuring the strength of Beidou signal and noise level. The higher the transmission carrier-to-noise ratio, the stronger the signal power relative to the noise, and the smaller the error of the observation data. The phase cycle slip is the instantaneous jump of the carrier phase observation value, which directly leads to positioning error.

[0060] The coordinate quality evaluation parameters at each epoch are compared with the predefined coordinate quality evaluation reference parameters. If the coordinate quality evaluation parameters at each epoch are all greater than or equal to the coordinate quality evaluation reference parameters, the absolute coordinates of the building control points at each epoch do not need to be optimized. If the coordinate quality evaluation parameter at a certain epoch is less than the coordinate quality evaluation reference parameter, the absolute coordinates of the building control points at the epoch are removed, and the optimization of the absolute coordinates of the building control points at each epoch is completed.

[0061] The absolute coordinates of the building control points at several epochs after the statistical optimization are recorded as the effective absolute coordinates of the building control points at each epoch. The effective absolute coordinates correspond to the epochs in the order of sampling, forming an absolute epoch sequence.

[0062] Based on the effective absolute coordinates of the building control points at each epoch, the first epoch in the absolute epoch sequence is recorded as the standard epoch, and the effective absolute coordinates of the building control points at each epoch are respectively epoch-differenced with the effective absolute coordinates of the building control points at the standard epoch to obtain the absolute deformation time sequence of the building control points. The arrangement order of the absolute deformation time sequence is sorted according to the absolute epoch sequence.

[0063] In this embodiment, the absolute deformation time sequence (unified to the national geodetic coordinate and elevation reference, and purified by quality body inspection and decomposition) is obtained. All monitoring points are "nailed" on the same ruler, and the deformation across floors, seasons, and devices can be directly compared. The threshold and classification caliber are unified, and the false alarm rate is reduced. The trend, rate, and acceleration and interlayer drift ratio can be calculated stably to support early warning and residual risk assessment. Reliable input is provided for GIS to generate regionalized risk surface and settlement gradient, and the "where and how fast it is changing" is intuitively presented to link fences and disposal. Furthermore, it is convenient to do scenario-based threshold self-adaptation and multi-source fusion (inclination, crack, acceleration) with meteorological and load factors, improve the judgment credibility, form a traceable evidence chain and compliance report basis, serve equipment health diagnosis, operation and decision-making, and review, and thus improve the monitoring accuracy, early warning timeliness, and command decision granularity as a whole.

[0064] Further, the optimization of the absolute coordinates of the building control points at each epoch further includes: The time interval of the adjacent two epochs in the absolute epoch sequence is extracted, which is recorded as the gap of each adjacent epoch.

[0065] The gap of each adjacent epoch is compared with the predefined epoch gap to obtain a gap comparison result. Based on the gap comparison result, the GIS platform determines whether to discard the gap of each adjacent epoch.

[0066] The gap comparison result includes a first gap comparison result and a second gap comparison result.

[0067] The first gap comparison result indicates that the gap of each adjacent epoch point is smaller than the epoch point defined gap, and the second gap comparison result indicates that there is a gap of adjacent epoch point greater than or equal to the epoch point defined gap.

[0068] If the gap comparison result shows the first gap comparison result, the GIS platform determines that the adjacent epoch point gap is not discarded, indicating that the adjacent epoch point satisfies the feature extraction constraint, and if the gap comparison result shows the second gap comparison result, the GIS platform determines that the adjacent epoch point gap is discarded, and marks the adjacent epoch point gap on the coordinate monitoring time axis, indicating that the adjacent epoch point does not satisfy the feature extraction constraint.

[0069] The housing safety early warning determination module is used for limiting item decomposition of the absolute deformation time sequence, feature extraction of the decomposed absolute deformation time sequence, obtaining the extracted features of the absolute deformation time sequence, event detection of the housing safety combined with the original deformation time sequence and the GIS grid factor, and determining whether to warn the housing safety risk.

[0070] The above limiting item decomposition is specifically a constrained physical decomposition of the absolute deformation time sequence: first, complete mass body inspection and gap segmentation, and only keep the effective epoch points; then express the curve with a small number of interpretable limiting items, including slow trend (smooth and slope upper limit), day and night and seasonal cycle (only take a few harmonics and constrain the amplitude), temperature effect (including time lag and quadratic term, coefficient upper and lower limit), and if necessary, add rainfall lag and known event step; estimate each component under mass weighted fitting, and subtract the periodic term, temperature term, rain-induced term and step from the absolute deformation in turn to obtain the net deformation; finally, only calculate the change rate and acceleration on the continuous time interval from the net deformation, without crossing the gap. In this way, reversible fluctuations are removed, overfitting is avoided, and subsequent threshold determination, trend warning and regional risk surface are more stable, comparable and traceable.

[0071] Specifically, the feature extraction of the decomposed absolute deformation time sequence obtains the extracted features of the absolute deformation time sequence, and the specific extraction process is as follows: The decomposed absolute deformation time sequence is denoted as the net deformation time sequence, the net deformation of the adjacent epoch point is obtained, and the net deformation of the current epoch point and the net deformation of the previous epoch point are differentiated according to the net deformation time sequence to obtain the deformation growth rate of the adjacent epoch point.

[0072] The net deformation of the current epoch point on the Z-axis and the net deformation of the previous epoch point on the Z-axis are extracted and difference processed to obtain the deformation displacement of the adjacent epoch point on the Z-axis.

[0073] Obtaining the adjacent floor height of the house, wherein the adjacent floor height can be extracted in the basic information record of the house, and the deformation displacement of the adjacent epoch point in the Z axis is calculated by finite difference approximation to obtain the house inter-floor drift ratio of the adjacent epoch point. The finite difference approximation can be brought into the formula:

[0074] In the formula, is the house inter-floor drift ratio of the adjacent epoch point, which can be understood as the average shear deformation rate / rotation angle of the layer, u(z) is the floor transverse displacement in a certain horizontal direction, which is a function of height z, z is the vertical height coordinate, z i , z i-1 is the upper and lower floor elevation of the layer, H is the adjacent floor height of the house, u(z i ), u(z i-1 ) are the transverse displacements at the upper and lower floor surfaces respectively, is the inter-floor relative displacement of the layer.

[0075] The extraction features of the absolute deformation time sequence include the house net deformation of the adjacent epoch point, the house deformation growth rate of the adjacent epoch point, and the house inter-floor drift ratio of the adjacent epoch point.

[0076] Further, it is determined whether to issue a house safety risk warning, and the specific determination process is as follows: The extraction features of the original deformation time sequence include the house original net deformation of the adjacent epoch point, the house original deformation growth rate of the adjacent epoch point, and the house original inter-floor drift ratio of the adjacent epoch point.

[0077] According to the house original net deformation of the adjacent epoch point, a net deformation threshold adjustment element is matched, specifically, the house original net deformation of the adjacent epoch point is matched with the net deformation threshold adjustment element corresponding to each house original net deformation interval in the risk monitoring and management library, the specific interval of the house original net deformation of the adjacent epoch point is determined, the net deformation threshold adjustment element corresponding to the interval is obtained, and the net deformation reference threshold is coupled with the predefined net deformation reference threshold, specifically, the net deformation threshold adjustment element is multiplied by the net deformation reference threshold to obtain the net deformation reference threshold.

[0078] The house original deformation growth rate of the adjacent epoch point is matched to obtain a deformation growth rate threshold adjustment element, specifically, the house original deformation growth rate of the adjacent epoch point is matched with the deformation growth rate threshold adjustment element corresponding to each house original deformation growth rate interval in the risk monitoring and management library, the specific interval of the house original deformation growth rate of the adjacent epoch point is determined, the deformation growth rate threshold adjustment element corresponding to the interval is obtained, and the deformation growth rate reference threshold is coupled with the predefined deformation growth rate reference threshold, specifically, the deformation growth rate threshold adjustment element is multiplied by the deformation growth rate reference threshold to obtain the deformation growth rate reference threshold.

[0079] The original inter-story drift ratio of the house of the adjacent epoch point is matched to obtain the drift ratio threshold adjustment element, specifically, the original inter-story drift ratio of the adjacent epoch point is matched with the drift ratio threshold adjustment element corresponding to each original inter-story drift ratio interval of the house in the risk monitoring management library, the specific interval of the original inter-story drift ratio of the adjacent epoch point is determined, the drift ratio threshold adjustment element corresponding to the interval is obtained, and the inter-story drift ratio reference threshold is obtained by coupling with the predefined inter-story drift ratio reference threshold.

[0080] It needs to be explained that the above-mentioned net deformation threshold adjustment element, deformation growth rate threshold adjustment element and drift ratio threshold adjustment element are all reduced with the increase of the original net deformation of the house, the original deformation growth rate of the house and the original inter-story drift ratio of the house. The greater the original net deformation of the house, the original deformation growth rate of the house and the original inter-story drift ratio of the house, the higher the potential safety risk of the house, and the threshold should be tightened to improve the sensitivity.

[0081] The preset GIS grid factor is coupled with the inter-story drift ratio reference threshold to obtain the inter-story drift ratio adaptive threshold.

[0082] The coupling between the preset GIS grid factor and the inter-story drift ratio reference threshold is specifically obtained by inputting the scenario threshold adjustment formula:

[0083] In the formula, is the inter-story drift ratio adaptive threshold, is the inter-story drift ratio reference threshold, is the preset GIS grid factor, g is the working condition correction function, which adjusts the threshold up or down according to the current environment, are respectively wind / rain / seismic intensity factors, which normalize wind speed, rain intensity, earthquake (PGA / intensity) and other physical quantities to 0-1.

[0084] In the stable state: ≈0, g≈1, at this time the inter-story drift ratio reference threshold is used .

[0085] In the extreme working condition: becomes larger, g<1, so that the inter-story drift ratio reference threshold is multiplied by a small number, and the judgment is more sensitive (earlier warning).

[0086] The net deformation of the adjacent epoch point, the deformation growth rate of the adjacent epoch point and the inter-story drift ratio of the adjacent epoch point are compared with the net deformation reference threshold, the deformation growth rate reference threshold and the inter-story drift ratio adaptive threshold respectively. If any one of the extracted features of the absolute deformation time sequence satisfies greater than the corresponding threshold, it is determined as a transient risk event, otherwise it is determined as a continuous static event.

[0087] If the house safety risk monitoring is a transient risk event, a pre-warning prompt is given in real time for the house safety risk, and if the house safety risk monitoring is a continuous static event, the house safety risk is continuously monitored, and the house safety risk score is determined.

[0088] In this embodiment, the dual-track strategy of pre-warning of transient events combined with continuous event monitoring and scoring is adopted, which can simultaneously improve the timeliness and stability, gives a pre-warning prompt in real time for the sudden risk (such as strong wind vibration, impact, abnormal acceleration), significantly compresses the discovery-response delay and triggers encrypted sampling, evidence preservation and personnel reminder, reduces the false negatives and secondary risks; continuously tracks the net deformation, rate and interlayer drift ratio of the slowly changing risk (such as progressive settlement, slow crack expansion), calculates the risk score, filters short-time noise, suppresses false positives, forms a comparable and traceable quantitative classification, and facilitates the development of disposal and operation plans according to the urgency; overall, it ensures that “the urgent is seen early” and “the slow change is accurately seen”, thereby improving the early warning lead time, reducing false positives and false negatives, optimizing resource input, and supporting more granular linkage decision and emergency command.

[0089] Specifically, the house safety risk is continuously monitored, and the specific monitoring process is as follows: According to the extraction features of the absolute deformation time sequence, the house safety risk score is determined in real time.

[0090] The house safety risk score is compared with the predefined risk score intervals, and each risk score interval includes a risk score first interval, a risk score second interval, a risk score third interval, and a risk score fourth interval. Each risk score interval is sequentially defined and incrementally ranked according to the sequence value.

[0091] Example: Take the risk score first interval threshold as t1, the risk score second interval threshold as t2, the risk score third interval threshold as t3, and the risk score fourth interval as t4, where t1 < t2 < t3 < t4.

[0092] If the house safety risk score belongs to the risk score first interval, the house safety is continuously monitored, and the adjacent epoch point time interval factor is matched according to the house safety risk score. Specifically, the adjacent epoch point time interval factor corresponding to the risk score first interval is extracted, and coupled with the currently set adjacent epoch point time interval. Specifically, the adjacent epoch point time interval factor is multiplied by the adjacent epoch point time interval to obtain an adjacent epoch point adaptive time interval, which is used to update the adjacent epoch point time interval and configure the next round of monitoring process.

[0093] If the house safety risk score belongs to the second interval of risk score, the GIS platform starts the rapid review instruction, extracts the deformation direction of the adjacent building control point of the house and the deformation amplitude of the adjacent building control point of the house, wherein the deformation direction and the deformation amplitude can be obtained in the monitoring record of the GIS platform, and it is judged whether the deformation direction and the deformation amplitude of the adjacent building control point are consistent, if the adjacent building control points are consistent, the house safety is continuously monitored, if a building control point is inconsistent, the weight factor of the extraction feature of the building control point in the house safety risk score is reduced, until the weight factor is defined, and the house safety is continuously monitored again.

[0094] Wherein the consistent is specifically represented as two points being dominated by the same load and the same structural working mode (the same rigid block translation / slight torsion, or the approximate consistent response of the same component under low curvature bending), without abnormal local differential deformation.

[0095] If the house safety risk score belongs to the third interval of risk score, the partition inspection is triggered, and the BDS short message redundancy is reported on the GIS platform, including the house coordinates, the house safety risk score, the trigger factor and the suggestion code.

[0096] If the house safety risk score belongs to the fourth interval of risk score, the risk warning of house safety is carried out, the full amount of original monitoring data is reserved, the geographic fence is formed on the preset GIS map, and the transmission carrier-to-noise ratio reference threshold correction element is matched according to the house safety risk score. Specifically, the transmission carrier-to-noise ratio reference threshold correction element corresponding to the fourth interval of risk score is extracted, and the preset transmission carrier-to-noise ratio is coupled, specifically, the transmission carrier-to-noise ratio reference threshold correction element is multiplied by the preset transmission carrier-to-noise ratio to obtain the transmission carrier-to-noise ratio defined threshold.

[0097] In the process of optimizing the absolute coordinates of each building control point of the house at each epoch point, the monitoring end transmission carrier-to-noise ratio at each epoch point is also compared with the transmission carrier-to-noise ratio defined threshold. If the monitoring end transmission carrier-to-noise ratio at a certain epoch point is less than the transmission carrier-to-noise ratio defined threshold, the absolute coordinate data corresponding to the epoch point is excluded and does not participate in the construction of the absolute deformation time sequence of the house building control point. If the monitoring end transmission carrier-to-noise ratio at each epoch point is greater than or equal to the transmission carrier-to-noise ratio defined threshold, the absolute coordinate data at each epoch point is retained.

[0098] In this embodiment, the risk early warning combined with the grading response mechanism can improve the timeliness and reliability: sudden abnormalities are quickly identified and immediately reminded, significantly shortening the time from discovery to disposal; slowly changing risks are continuously monitored and scored quantitatively, suppressing short-time noise and reducing false positives and false negatives; according to the information, early warning, alarm, emergency, grading sampling, fence, review and disposal, resources are accurately invested according to the risk level; the whole process retains evidence and parameter backwriting, forms a traceable and self-adapting closed loop, and improves the granularity and executability of command and decision-making.

[0099] Further, the housing safety risk score is determined, and the specific determination process is as follows: The extraction features of the absolute deformation time sequence also include the crack width growth rate of the adjacent epoch point.

[0100] The crack width growth rate threshold adjustment element is matched by extracting the crack width growth rate of the adjacent epoch point. Specifically, the crack width growth rate threshold adjustment element corresponding to the interval of the crack width growth rate of the adjacent epoch point is matched with the crack width growth rate of the adjacent epoch point, the interval to which the crack width growth rate of the adjacent epoch point belongs is determined, and the crack width growth rate threshold adjustment element corresponding to the interval is obtained. Coupling with the predefined crack width reference growth rate, specifically, the crack width reference growth rate is obtained by multiplying the crack width growth rate threshold adjustment element and the crack width reference growth rate.

[0101] The crack width growth rate threshold adjustment element monotonically decreases with the crack width original growth rate, and the greater the crack width original growth rate, the smaller the crack width growth rate threshold adjustment element obtained. When the measured crack growth rate is greater, the risk is higher, and the threshold should be tightened to improve the sensitivity, so the adjustment element should decrease with the increase of the growth rate.

[0102] The housing net deformation of the adjacent epoch point, the housing deformation growth rate of the adjacent epoch point, the housing interlayer drift ratio of the adjacent epoch point, and the housing crack width growth rate of the adjacent epoch point are respectively deviated from the net deformation reference threshold, the deformation growth rate reference threshold, the interlayer drift ratio adaptive threshold, and the crack width reference growth rate. The deviation processing results are sequentially weighted and aggregated by introducing a weight factor to obtain the housing safety risk score, and the specific analysis method is as follows:

[0103] In the formula, R is the housing safety risk score, H is the net deformation of the adjacent epoch point, is the net deformation reference threshold, is the housing deformation growth rate of the adjacent epoch point, is the deformation growth rate reference threshold, and V is the housing interlayer drift ratio of the adjacent epoch point. is the inter-story drift ratio, W is the crack width growth rate of the adjacent epoch point, is the crack width reference growth rate, a1 is the weight factor corresponding to the net deformation of the building in the risk monitoring management library, a2 is the weight factor corresponding to the deformation growth rate of the building in the risk monitoring management library, a3 is the weight factor corresponding to the inter-story drift ratio of the building in the risk monitoring management library, and a4 is the weight factor corresponding to the crack width growth rate of the building in the risk monitoring management library.

[0104] The corresponding relationship between the indicators and the weights defined in advance in the risk monitoring management library is that the monitoring system first establishes a set of mapping for the net deformation of the building, the deformation growth rate, the inter-story drift ratio, the crack width growth rate, etc. During operation, only the real-time indicator value needs to be substituted into the corresponding mapping, and the net deformation weight, the growth rate weight, the inter-story drift ratio weight, and the crack growth rate weight can be directly read out. The mapping set is based on historical monitoring and event samples, combined with structure type and importance level, and is grouped and statistically analyzed according to different working conditions (wind, rain, earthquake, etc.). The mapping set is binning and monotonically constrained fitting (or piecewise linear / spline interpolation) according to the expert threshold and the upper / lower limit of experience, and then is calibrated by replay and cross-validation to reduce false positives / misses, and is solidified into the library and versioned management, so as to ensure consistency while considering scene adaptation.

[0105] In this embodiment, through the multivariate analysis of the net deformation of the building, the deformation growth rate, the inter-story drift ratio, and the crack width growth rate, the correlation of these parameters is considered. The net deformation is the displacement state, the deformation growth rate is the first-order difference (the difference between the displacements of adjacent epoch points / time), which reflects how fast it changes. The inter-story drift ratio is the normalization (divided by the story height) of the displacement difference between adjacent floors, which is equal to the spatial gradient of the horizontal displacement. The crack width growth rate is driven by the drift ratio and the growth rate, and is often manifested as a lagging uplift. In the small deformation stage, these quantities should be consistent in direction and the amplitude should be smooth over time. When the growth rate / drift ratio exceeds the threshold or appears positive acceleration in the continuous epoch point, the crack growth rate rises and feedback reduces the stiffness of the component, so that the net deformation and the drift ratio are further amplified under the same external load in the subsequent stage, forming a closed loop of displacement, velocity, crack, stiffness degradation, and larger displacement. Therefore, the engineering judgment needs to verify the consistency of the direction / amplitude / correlation in the same time window, and cannot be calculated across the gap, so as to distinguish the real evolution from the noise jump.

[0106] The regionalized risk surface visualization module is used for extracting features based on the absolute deformation time sequence to generate a regionalized risk surface, and the regionalized risk surface is visualized and circled on the preset GIS map of the building safety monitoring area to form a geographic fence, and the intelligent monitoring of the building safety risk is completed.

[0107] Under the unified coordinate and national height datum, comparable features (such as net deformation, change rate, interlayer drift ratio, crack growth rate, etc.) of absolute deformation time series of each monitoring point are extracted according to the current time window, and after shielding of poor epoch points and gaps combined with quality weight and scenario threshold, the point features are generated into continuous regionalized risk surface through quality weighted interpolation (such as inverse distance or Kriging); then, the risk surface is graded rasterized and contour extracted according to the grading threshold, and a set of super-limit regional polygons are obtained through connected domain aggregation, smoothing and topological correction; these polygons are superimposed on the preset house safety monitoring area basic GIS layer, and the attributes such as level, feature source, time window and confidence are written, that is, the visual geographic fence is formed, and is refreshed with new data, so as to realize the dynamic delineation and linkage control of 'where is changing and how fast it is changing'.

[0108] Referring to Figure 2 The second aspect of the present application provides a house safety risk intelligent monitoring method based on Beidou and GIS platform, comprising: constructing a reference network in a house safety monitoring area according to the preset reference station layout coordinates and the layout coordinates of each building control point of the house, extracting the relative coordinates of each building control point of the house relative to the reference station for epoch difference statistics, and forming the original deformation time series of each building control point of the house.

[0109] The absolute coordinates of each building control point of the house are monitored by the Beidou monitoring end, and the absolute coordinates are epoch-differenced to form the absolute deformation time series of each building control point of the house.

[0110] The absolute deformation time series is decomposed into limited terms, and the decomposed absolute deformation time series is extracted to obtain the extracted features of the absolute deformation time series, and the original deformation time series and GIS grid factors are combined to detect the house safety event and determine whether to warn the house safety risk.

[0111] The extracted features of the absolute deformation time series are generated into a regionalized risk surface, and the regionalized risk surface is formed into a geographic fence on the preset GIS map of the house safety monitoring area to realize the visual delineation and complete the house safety risk intelligent monitoring.

[0112] The above content is only an example and description of the structure of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the structure of the present application or exceed the scope defined by the present application, which shall belong to the protection scope of the present application.

Claims

1. A house safety risk intelligent monitoring system based on Beidou and GIS platform, characterized in that, The method comprises the following steps: An original deformation time sequence statistical module is used to construct a reference network in a house safety monitoring area according to preset reference station layout coordinates and house building control point layout coordinates, to extract relative coordinates of the house building control points relative to the reference station for epoch difference statistics, and to form original deformation time sequences of the house building control points; An absolute deformation time sequence statistical module is used to monitor the coordinates of the house building control points based on a Beidou monitoring terminal, to obtain absolute coordinates of the house building control points, to perform epoch difference statistics on the absolute coordinates, and to form absolute deformation time sequences of the house building control points; A house safety early warning judgment module is used to perform limited term decomposition on the absolute deformation time sequences, to perform feature extraction on the decomposed absolute deformation time sequences, to obtain extracted features of the absolute deformation time sequences, to perform event detection on house safety in combination with the original deformation time sequences and GIS grid factors, and to determine whether to issue a house safety risk early warning; A regionalized risk surface visualization module is used to generate a regionalized risk surface based on the extracted features of the absolute deformation time sequences, to form a geographic fence on a preset GIS map of the house safety monitoring area to visually delineate the regionalized risk surface, and to complete intelligent monitoring of house safety risks.

2. The housing safety risk intelligent monitoring system based on Beidou and GIS platform according to claim 1, characterized in that: The reference station layout coordinates and the house building control point layout coordinates are specifically analyzed as follows: The maximum span of the house safety monitoring area and the openness of the house safety monitoring area are obtained, and the construction characteristic value of the house safety monitoring area is obtained by coupling them together; The construction characteristic value of the house safety monitoring area is matched with the reference station layout number corresponding to each construction characteristic value interval to obtain a reference station layout adaptive number; The maximum elevation of the house is obtained, and the construction influence quantity of the house is obtained by coupling the maximum elevation with the construction characteristic value of the house safety monitoring area; The construction influence quantity of the house is matched with the building control point layout number corresponding to each construction influence quantity interval, and the obtained building control point layout number is recorded as a building control point layout adaptive number; The reference station layout adaptive number, the building control point layout adaptive number, and a preset layout constraint set are input into a mixed integer linear programming algorithm, and the reference station layout coordinates and the house building control point layout coordinates are output; The reference network is constructed based on the reference station layout coordinates and the house building control point layout coordinates. 3.The housing safety risk intelligent monitoring system based on Beidou and GIS platform according to claim 1, characterized in that: The original deformation time sequences of the house building control points are formed by specifically analyzing the following process: At each epoch point, the relative coordinates of the house building control points relative to the reference station are extracted to obtain relative coordinates of the house building control points at each epoch point, wherein the epoch point represents a sampling time point or a sampling time; The epoch point at which the relative coordinates are first extracted is recorded as a reference epoch point, and the relative coordinates of the house building control points at the current epoch point are subtracted from the relative coordinates of the corresponding building control points at the reference epoch point to obtain the relative coordinate deviation of each building control point at the epoch point, wherein the epoch difference represents the relative coordinate deviation between the current epoch point and the reference epoch point. The relative coordinate deviations of each building control point of the house at different epoch difference points are counted, and the original deformation time sequence of each building control point of the house is obtained by sorting according to the relative epoch point sequence.

4. The housing safety risk intelligent monitoring system based on Beidou and GIS platform according to claim 1, characterized in that: The absolute deformation time sequence of each building control point of the house is formed, and the specific analysis process is as follows: The Beidou monitoring end monitors the absolute coordinates of each building control point of the house in real time, and the absolute coordinates of each building control point of the house at each epoch point are obtained. The absolute coordinates of each building control point of the house at each epoch point are optimized, specifically as follows: The GIS platform receives the absolute coordinate monitoring data transmitted by the Beidou monitoring end in real time, extracts the transmission carrier-to-noise ratio of the Beidou monitoring end at each epoch point and the phase cycle slip of the Beidou monitoring end at each epoch point, and combines the quality evaluation influence elements of the monitoring openness of the house safety monitoring area to obtain the coordinate quality evaluation parameters at each epoch point. The coordinate quality evaluation parameters at each epoch point are compared with the predefined coordinate quality evaluation reference parameters. If the coordinate quality evaluation parameters at each epoch point are greater than or equal to the coordinate quality evaluation reference parameters, the absolute coordinates do not need to be optimized. If the coordinate quality evaluation parameters at a certain epoch point are less than the coordinate quality evaluation reference parameters, the absolute coordinates of each building control point of the house at the epoch point are removed, and the optimization of the absolute coordinates of each building control point of the house at each epoch point is completed. The absolute coordinates of each building control point of the house at several epoch points after optimization are counted, which are recorded as the effective absolute coordinates of each building control point of the house at each epoch point. The effective absolute coordinates correspond to each epoch point to form an absolute epoch point sequence according to the sampling order. Based on the effective absolute coordinates of each building control point of the house at each epoch point, the first epoch point in the absolute epoch point sequence is recorded as the standard epoch point, and the effective absolute coordinates of each building control point of the house at each epoch point are compared with the effective absolute coordinates of each building control point of the house at the standard epoch point to obtain the absolute deformation time sequence of each building control point of the house. The arrangement order of the absolute deformation time sequence is sorted according to the absolute epoch point sequence.

5. The housing safety risk intelligent monitoring system based on the Beidou and GIS platform according to claim 4, characterized in that: The optimization of the absolute coordinates of each building control point of the house at each epoch point also includes: The time interval of several adjacent epoch points in the absolute epoch point sequence is extracted, which is recorded as the gap of each adjacent epoch point. The gap of each adjacent epoch point is compared with the predefined epoch point boundary gap to obtain a gap comparison result, and the GIS platform determines whether to discard the gap of each adjacent epoch point based on the gap comparison result. The gap comparison result includes a gap comparison first result and a gap comparison second result. The gap comparison first result indicates that each adjacent epoch point gap is less than the epoch point boundary gap, and the gap comparison second result indicates that there is an adjacent epoch point gap greater than or equal to the epoch point boundary gap. If the gap comparison result shows the gap comparison first result, the GIS platform determines that the gap of the adjacent epoch point is not discarded, indicating that the adjacent epoch point meets the feature extraction constraint; if the gap comparison result shows the gap comparison second result, the GIS platform determines that the gap of the adjacent epoch point is discarded, and marks the gap of the adjacent epoch point as missing on the coordinate monitoring time axis, indicating that the adjacent epoch point does not meet the feature extraction constraint.

6. The housing safety risk intelligent monitoring system based on Beidou and GIS platform according to claim 1, characterized in that: The decomposed absolute deformation time sequence is denoted as a net deformation time sequence, and the net deformation of the house of the adjacent epoch point is obtained, and the net deformation of the current epoch point and the net deformation of the last epoch point are derived according to the net deformation time sequence, and the house deformation growth rate of the adjacent epoch point is obtained. The net deformation of the current epoch point on the Z-axis and the net deformation of the last epoch point on the Z-axis are extracted, and difference processing is performed to obtain the deformation displacement of the adjacent epoch point on the Z-axis. The adjacent story height of the house is obtained, and the deformation displacement of the adjacent epoch point on the Z-axis is subjected to finite difference approximation calculation to obtain the house story drift ratio of the adjacent epoch point. The extraction features of the absolute deformation time sequence include the net deformation of the house of the adjacent epoch point, the house deformation growth rate of the adjacent epoch point, and the house story drift ratio of the adjacent epoch point. The determination of whether to warn of the house safety risk is specifically determined as follows:

7. The housing safety risk intelligent monitoring system based on the Beidou and GIS platform according to claim 4, characterized in that: The extraction features of the original deformation time sequence include the original net deformation of the house of the adjacent epoch point, the original house deformation growth rate of the adjacent epoch point, and the original house story drift ratio of the adjacent epoch point. According to the original net deformation of the house of the adjacent epoch point, a net deformation threshold adjustment element is matched to obtain a net deformation reference threshold value coupled with a predefined net deformation reference threshold value to obtain a net deformation reference threshold value. The original house deformation growth rate of the adjacent epoch point is matched to obtain a deformation growth rate threshold adjustment element, which is coupled with a predefined deformation growth rate reference threshold value to obtain a deformation growth rate reference threshold value. The original house story drift ratio of the adjacent epoch point is matched to obtain a drift ratio threshold adjustment element, which is coupled with a predefined story drift ratio reference threshold value to obtain a story drift ratio reference threshold value. A preset GIS grid factor is coupled with the story drift ratio reference threshold value to obtain a story drift ratio adaptive threshold value. The net deformation of the house of the adjacent epoch point, the house deformation growth rate of the adjacent epoch point, and the house story drift ratio of the adjacent epoch point are compared with the net deformation reference threshold value, the deformation growth rate reference threshold value, and the story drift ratio adaptive threshold value, respectively. If any one of the extraction features of the absolute deformation time sequence satisfies greater than the corresponding threshold value, it is determined as a transient risk event, otherwise it is determined as a continuous static event. If the house safety risk monitoring is a transient risk event, a pre-warning prompt is given for the house safety risk in real time, and if the house safety risk monitoring is a continuous static event, the house safety risk is continuously monitored, and the house safety risk score is determined. The continuous monitoring of the house safety risk is specifically monitored as follows:

8. The housing safety risk intelligent monitoring system based on the Beidou and GIS platform according to claim 7, characterized in that: The house safety risk score is determined in real time according to the extraction features of the absolute deformation time sequence. ​ The housing safety risk score is compared with predefined risk score intervals, including a risk score first interval, a risk score second interval, a risk score third interval, and a risk score fourth interval; The risk score intervals are sequentially defined and incrementally ranked in order of numerical value; If the housing safety risk score belongs to the risk score first interval, the housing safety is continuously monitored, and the housing safety risk score is matched to obtain an adjacent epoch point interval factor, which is coupled with the current adjacent epoch point time interval to obtain an adaptive adjacent epoch point time interval for updating the adjacent epoch point time interval and configuring the next round of monitoring process; If the housing safety risk score belongs to the risk score second interval, the GIS platform starts a rapid review instruction, extracts the deformation direction of the adjacent building control points of the housing and the deformation amplitude of the adjacent building control points of the housing, determines whether the deformation direction and the deformation amplitude of the adjacent building control points are consistent, if the adjacent building control points are consistent, the housing safety is continuously monitored, if a building control point is inconsistent, the weight factor of the building control point extraction feature in the housing safety risk score is reduced, and the housing safety is continuously monitored again; If the housing safety risk score belongs to the risk score third interval, the partition inspection is triggered, and the GIS platform reports a BDS short message redundancy, including the housing coordinates, the housing safety risk score, the trigger factor, and the suggestion code; If the housing safety risk score belongs to the risk score fourth interval, a risk warning is given to the housing safety, all original monitoring data are reserved, a geographic fence is formed on a preset GIS map, and a transmission carrier-to-noise ratio reference threshold is matched according to the housing safety risk score, which is coupled with a preset transmission carrier-to-noise ratio to obtain a transmission carrier-to-noise ratio defined threshold. The optimization of the absolute coordinates of each building control point of the housing at each epoch point further includes comparing the monitoring end transmission carrier-to-noise ratio at each epoch point with the transmission carrier-to-noise ratio defined threshold, and if the monitoring end transmission carrier-to-noise ratio at a certain epoch point is less than the transmission carrier-to-noise ratio defined threshold, the corresponding absolute coordinate data at the epoch point is removed and does not participate in the construction of the absolute deformation time sequence of the housing building control points. 9.The housing safety risk intelligent monitoring system based on the Beidou and GIS platform according to claim 8, characterized in that: The specific determination process of the housing safety risk score includes: The extraction feature of the absolute deformation time sequence further includes a housing crack width growth rate of adjacent epoch points; An original housing crack width growth rate of adjacent epoch points is extracted, a crack width growth rate threshold adjustment element is matched to obtain a crack width reference growth rate by coupling with a predefined crack width reference growth rate; The housing net deformation of adjacent epoch points, the housing deformation growth rate of adjacent epoch points, the housing interlayer drift ratio of adjacent epoch points, and the housing crack width growth rate of adjacent epoch points are respectively subjected to deviation processing with a net deformation reference threshold, a deformation growth rate reference threshold, an interlayer drift ratio adaptive threshold, and a crack width reference growth rate, and a weight factor is introduced to sequentially weight and aggregate the deviation processing results to obtain a housing safety risk score.

10. The method for intelligent monitoring of housing safety risk based on Beidou and GIS platform, applied to the system for intelligent monitoring of housing safety risk based on Beidou and GIS platform in any one of claims 1-9, characterized in that: The specific determination process of the housing safety risk score includes: In the house safety monitoring area, a reference network is constructed according to preset reference station layout coordinates and building control point layout coordinates of the house, relative coordinates of the building control points of the house relative to the reference station are extracted for epoch difference statistics to form original deformation time series of the building control points of the house; Absolute coordinates of the building control points of the house are obtained by monitoring the building control points of the house based on a Beidou monitoring terminal, and absolute deformation time series of the building control points of the house are formed by epoch difference statistics on the absolute coordinates; The absolute deformation time series is decomposed into limited terms, and the extracted features of the decomposed absolute deformation time series are obtained by feature extraction, and event detection is performed on the house safety in combination with the original deformation time series and GIS grid factors to determine whether to issue a house safety risk warning; A regionalized risk surface is generated based on the extracted features of the absolute deformation time series, and the regionalized risk surface is visualized and circled on a preset GIS map of the house safety monitoring area to complete intelligent monitoring of the house safety risk.

Images