A method of water conservancy project monitoring data management based on BIM

Abstract

The present invention relates to the technical field of monitoring data management, and particularly to a method for managing water conservancy project monitoring data based on BIM. This method analyzes the stability of water conservancy data in the time series of monitoring points, preliminarily screens out stable reference points, further analyzes the consistency of the stable reference points and local reference points at each moment, and screens out available reference points; at the same time, considering the aggregation of the available reference points selected in terms of regional distribution density, the final reference points are determined; finally, combined with the unstable reference points and the reduced final reference points for compressed storage. The present invention comprehensively evaluates the local time series and distribution density of the monitoring points with stable data, reduces the necessary storage quantity of the monitoring points, reduces the storage pressure, effectively improves the backup quantity of BIM water conservancy data, and further improves the backup efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring data management, and in particular to a method for managing monitoring data of a water conservancy project based on BIM. Background Art

[0002] Water conservancy project data includes hydrological monitoring data, water quality monitoring data and project structure data, which are direct information for evaluating and observing water conservancy conditions. Water conservancy project data monitoring is not only an observation of the project's operating status, but monitoring water conservancy project data is an effective means and way to ensure the safe and efficient operation of water conservancy facilities and improve the management level of related projects. Therefore, water conservancy BIM came into being, which contains all the information of water conservancy projects, can analyze, simulate, and warn projects in a virtual environment, and share information with many departments. However, while BIM brings convenience, it is also accompanied by the possibility of many data being tampered with, so the security of BIM data needs to be guaranteed.

[0003] In order to ensure the security of BIM water conservancy data, the monitored water conservancy data will be backed up regularly and a complete recovery mechanism will be established. When the data is lost or damaged, the data can be restored in time to ensure the continuity of water conservancy project monitoring. Commonly used backup methods now include full backup, differential backup and incremental backup. Although these backup methods can fully guarantee the security of data, the amount of monitoring data of water conservancy projects is extremely large, and the servers used for backup are limited, resulting in the inability to complete the backup of many water conservancy data. The pressure of stored backup data is too high, affecting the efficient operation of BIM water conservancy data during analysis. Summary of the invention

[0004] In order to solve the technical problem that the amount of monitoring data of water conservancy projects in the prior art is extremely large and the servers used for backup are limited, resulting in the inability to complete the backup of numerous water conservancy data, the purpose of the present invention is to provide a water conservancy project monitoring data management method based on BIM, and the technical scheme adopted is as follows:

[0005] The present invention provides a water conservancy project monitoring data management method based on BIM, the method comprising:

[0006] For each hydraulic parameter of the BIM hydraulic data to be backed up, obtain the parameter values ​​of all monitoring points at each sampling time;

[0007] According to the distribution stability of the parameter values ​​at each monitoring point at all sampling moments, the stable reference points are screened out; according to the distribution of the parameter values ​​between the stable reference points and the local monitoring points at each sampling moment, the local trend of each stable reference point at the sampling moment is obtained; according to the deviation of the local trend at each stable reference point in time series, the available reference points are screened out;

[0008] At each sampling moment, the distribution range of each available benchmark point in the local available benchmark point within the regional range is calculated to obtain the regional distribution density index of each available benchmark point; the initial benchmark point is determined from the available benchmark points based on the regional distribution density index; the parameter value changes between the available benchmark points are analyzed by extending the initial benchmark point in different preset directions, and a new initial benchmark point is determined and iteratively extended until the new initial benchmark point cannot be determined;

[0009] At each sampling moment, the final reference point at each sampling moment is screened out according to the proportion of each initial reference point as the initial reference point at all sampling moments; the data at all final reference points and unstable reference points in each water conservancy parameter of the BIM water conservancy data to be backed up are compressed and stored.

[0010] Furthermore, the method for obtaining the stable reference point includes:

[0011] For any monitoring point, calculate the z-score of the parameter value at each sampling time at the monitoring point as the standard value at each sampling time at the monitoring point;

[0012] The variance of the standard value at each sampling moment at the monitoring point is normalized by negative correlation mapping to obtain the time series stability index of the monitoring point;

[0013] The monitoring point where the timing stability index is greater than or equal to the preset stability threshold is used as the stability reference point.

[0014] Furthermore, the method for obtaining the local trend degree includes:

[0015] Each stable reference point is taken as the target point in turn; within the preset local range of the target point, each other monitoring point is taken as a local point of the target point; the distance between each local point and the target point is negatively correlated and normalized to obtain the distance weight of each local point;

[0016] At each sampling moment, the parameter values ​​of the local points are weighted and averaged based on the distance weight to obtain the local parameter value of the target point at each sampling moment; the difference between the parameter value of the target point and the local parameter value is negatively correlated and normalized to obtain the local trend degree of the target point at each sampling moment.

[0017] Furthermore, the method for obtaining the available reference points includes:

[0018] For any sampling moment, each sampling moment before the sampling moment is used as the reference moment of the sampling moment; the time difference between the sampling moment and each reference moment is negatively correlated and normalized to obtain the time weight of each reference moment;

[0019] For any stable reference point, the local trend degree of the stable reference point at the reference time is weighted based on the time weight and the average value is calculated to obtain the available index of the stable reference point;

[0020] A stable reference point whose usable index is greater than a preset usable threshold is regarded as a usable reference point.

[0021] Furthermore, the method for obtaining the regional distribution density index includes:

[0022] At each sampling time, for any available reference point, the regional distribution point of the available reference point is obtained according to the distance distribution of other available reference points;

[0023] The maximum distance between the available reference point and the regional distribution point is used as the distribution radius of the available reference point; a circle is drawn with the available reference point as the center and the distribution radius as the radius, which is used as the regional range of the available reference point;

[0024] The ratio between the total number of regional distribution points of the available benchmark point and the area of ​​the regional range is used as the regional distribution density index of the available benchmark point.

[0025] Furthermore, the method for acquiring the regional distribution points includes:

[0026] Calculate the distance between each other available reference point and the available reference point, and arrange all other available reference points in ascending order of distance to obtain a range available point sequence of the available reference point;

[0027] The first preset number of other available reference points in the range available point sequence are used as regional distribution points of the available reference point.

[0028] Furthermore, determining the initial reference point from available reference points based on the regional distribution density index includes:

[0029] At each sampling moment, the available reference point with the largest regional distribution density index is used as the initial reference point.

[0030] Further, the step of extending the initial reference point in different preset directions to analyze the parameter value changes between the available reference points, determining a new initial reference point, and iteratively extending until a new initial reference point cannot be determined includes:

[0031] According to the regional distribution density index and distribution radius of the initial reference point, the extension length of the initial reference point is obtained;

[0032] Extending in a preset extension direction with the initial reference point as the center, stopping the extension after reaching the extension length, and connecting every two adjacent points where the extension is stopped in the preset extension direction to obtain each extension range;

[0033] For any extended range, the distance between each available reference point in the extended range and the initial reference point is calculated, and the available reference points are arranged in order of distance from small to large to obtain an extended reference point sequence of the extended range;

[0034] Obtain a differential sequence of parameter values ​​of available reference points in the extended reference point sequence; traverse the differential sequence from front to back, and when there is a differential value whose absolute value is greater than a preset differential threshold, use the corresponding differential value as a stop differential value and stop traversal, and use the available reference point corresponding to the largest serial number in the extended reference point sequence corresponding to the stop differential value as a new initial reference point on the extended range;

[0035] All new initial reference points are iteratively extended to obtain new initial reference points until the new initial reference points cannot be determined.

[0036] Furthermore, the method for obtaining the extension length includes:

[0037] The value after normalization of the regional distribution density index of the initial benchmark point is used as the density influence coefficient of the initial benchmark point;

[0038] The product of the distribution radius of the initial reference point and the density influence coefficient is taken as the extension length of the initial reference point.

[0039] Furthermore, the method for obtaining the final reference point includes:

[0040] For any initial reference point at each sampling moment, count the number of times the initial reference point is used as the initial reference point at all sampling moments to obtain the total number of selections of the initial reference point; use the ratio of the total number of selections of the initial reference point to the total number of sampling moments as the screening index of the initial reference point;

[0041] The initial reference point whose screening index is greater than the preset screening threshold is used as the final reference point.

[0042] The present invention has the following beneficial effects:

[0043] The present invention first analyzes the stability of water conservancy data in the time series of monitoring points, and preliminarily selects stable benchmark points. In water conservancy projects, the interaction between water flow and soil will cause various characteristics such as water quality or seepage to change gently in space and time. Therefore, the monitoring points can be reduced by interpolating only necessary monitoring points to infer the actual data, so the monitoring points with stability are reduced. Further analyze the consistency of stable benchmark points and local benchmark points at individual moments, select benchmark points with better centralized stability, which can be used to characterize the local representative degree of data, and determine the available benchmark points with higher representation to reduce the monitoring points. At the same time, consider the aggregation of the available benchmark points selected in the regional distribution density, further reduce the representative monitoring points, and determine the final benchmark points. Finally, the unstable benchmark points and the reduced final benchmark points are combined for compressed storage. The present invention reduces the necessary storage number of monitoring points by conducting a comprehensive evaluation of the local time series and distribution density of the monitoring points with stable data, reduces the storage pressure, effectively increases the backup number of BIM water conservancy data, and further improves the backup efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flow chart of a method for managing water conservancy project monitoring data based on BIM provided by one embodiment of the present invention;

[0046] Figure 2 A flow chart of a method for obtaining regional distribution density provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the BIM-based water conservancy project monitoring data management method proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The following is a detailed description of a BIM-based water conservancy project monitoring data management method provided by the present invention in conjunction with the accompanying drawings.

[0050] See also Figure 1 , which shows a flow chart of a water conservancy project monitoring data management method based on BIM provided by an embodiment of the present invention, the method comprising the following steps:

[0051] S1: In the BIM water conservancy data to be backed up, obtain the parameter values ​​of the water conservancy parameters of all monitoring points at each sampling time.

[0052] The BIM water conservancy monitoring area needs to cover the main body of the dam, numerous river channels, surrounding geological environment, etc. The monitoring points need to be evenly distributed, and the density should be increased in key areas. It is planned to use the grid point method to build monitoring points in various places. By placing data acquisition devices at each monitoring point, including but not limited to water level sensors, flow sensors, suspended sediment samplers and other related devices, and combining laser technology with fiber optic sensing technology to monitor the deformation and displacement data of the dam. In an embodiment of the present invention, the acquisition frequency of all data acquisition devices is synchronized, that is, a parameter value of each water conservancy parameter can be obtained at the monitoring point at each sampling moment. The specific acquisition frequency implementer can adjust it according to the implementation scenario, and there is no restriction here.

[0053] The water conservancy parameters of the monitoring data include water level, flow, evaporation data, sediment content data, soil moisture in the surrounding area (soil moisture is mainly used to evaluate soil moisture content), water quality data in the water area, and deformation, stress, seepage data of water conservancy project structures, etc. Since the distribution and changes of different water conservancy parameters are different, each water flow parameter is analyzed separately to reduce the retention situation, that is, the subsequent analysis of the benchmark process is analyzed using a single water conservancy parameter as an example.

[0054] All water conservancy data monitored over a period of time will be backed up and stored. Since the collection location of the relevant monitoring data in the water conservancy projects is in a relatively closed and limited area, the data has a certain trend in spatial distribution, that is, the area between two locations can be roughly estimated based on the data of these two locations. By reducing and compressing the data of these calculable areas, the backup storage pressure can be reduced to a greater extent.

[0055] S2: According to the distribution stability of each water conservancy parameter at each monitoring point at all sampling times, the stable reference points are screened out; under each water flow parameter, according to the distribution of parameter values ​​between the stable reference point and the local monitoring point at each sampling time, the local trend of each stable reference point at the sampling time is obtained; according to the degree of deviation of the local trend at each stable reference point in time series, the available reference points under each water flow parameter are screened out.

[0056] At the same time, due to the continuous monitoring of water conservancy projects, the data obtained is constantly updated, which leads to certain time series characteristics of the relevant data of each monitoring point. Some monitoring points are located at the edge of cracks or polluted waters, resulting in constant fluctuations in data. For better analysis in the future, these data points need to be retained. In addition, some areas are in the center of the reservoir and are less affected by various situations. The monitoring data are relatively stable in time series. In order to ensure that more necessary backup storage data can be stored, these relatively stable data in time series can be reduced to reduce pressure.

[0057] When the water conservancy project is operating normally, the time series data of various parameters in the water area or water conservancy project will change in a relatively stable area. Therefore, the stable reference point is first selected according to the time series stability. In the embodiment of the present invention, the method for obtaining the stable reference point includes:

[0058] First, for any monitoring point, the z-score of the parameter value at each sampling moment at the monitoring point is calculated as the standard value at each sampling moment at the monitoring point. The z-score reflects the degree of deviation between the parameter value collected at each moment and the overall time series. The degree of deviation is used as the standard value for stability analysis, which can also eliminate the influence of different orders of magnitude when the stability situation is uniformly divided later. It should be noted that the z-score is a well-known technical means for those skilled in the art to calculate the value, which will not be elaborated here.

[0059] The variance of the standard value at each sampling moment at the monitoring point is further normalized by negative correlation mapping to obtain the time series stability index of the monitoring point. The stability of the deviation change is reflected by the variance. The smaller the variance, the higher the stability. It should be noted that negative correlation mapping and normalization processing are technical means well known to technical personnel in this field. For example, negative correlation mapping can adopt the inverse proportional or negative exponential power form, normalized sampling standard normalization method, etc., which are not limited or elaborated here.

[0060] Finally, the monitoring points whose timing stability index is greater than or equal to the preset stability threshold are used as stable reference points. In the embodiment of the present invention, the preset stability threshold is set to 0.65, and the specific value can be adjusted by the implementer. At this time, the stable reference points all have the characteristics of stable changes in timing, and the synchronous changes can be further analyzed, and the monitoring points with synchronous changes can be screened out to reduce storage pressure.

[0061] In the process of storing by reducing the number of monitoring points, in order to save storage space while ensuring that the data is recoverable, that is, the monitoring point data between the reference points can be restored by interpolation, it is necessary to perform local consistency analysis on each reference point and screen out points with higher representativeness based on local conditions.

[0062] Preferably, in an embodiment of the present invention, the method for obtaining the local trend degree includes:

[0063] Each stable reference point is taken as a target point in turn, and each reference point is analyzed. Within the preset local range of the target point, each other monitoring point is taken as a local point of the target point. In the embodiment of the present invention, the preset local range is set to be a range with the target point as the center and a side length of 3 meters. The specific value can be adjusted by the implementer.

[0064] Taking into account the diffusion and smoothing effects of water areas and soil in water conservancy projects, the closer the monitoring data is to the selected benchmark point, the higher the reference value. The distance between each local point and the target point is negatively correlated and normalized to obtain the distance weight of each local point. The distance is added as the analysis weight, and the smaller the distance, the greater the proportion.

[0065] At each sampling moment, the parameter values ​​of the local points are weighted and averaged based on the distance weight to obtain the local parameter value of the target point at each sampling moment. The distribution size of the local and overall parameter values ​​is integrated to reflect the concentration distribution of the parameter value. The difference between the parameter value of the target point and the local parameter value is further negatively correlated and normalized to obtain the local trend degree of the target point at each sampling moment. The smaller the difference between the target point and the overall local point, the higher the consistent concentration trend, and the greater the local trend degree of the target point.

[0066] Although evaluating the data consistency of each stable benchmark point and the surrounding monitoring points at a single moment can illustrate the feasibility of using the parameter value of the stable benchmark point at a single moment as the interpolation reference value of the surrounding monitoring points, for backup water conservancy data, in order to have more backup data, it is necessary to ensure the highest possible compression under the premise of accurate data. Therefore, it is necessary to select monitoring points that are relatively consistent in the entire time series.

[0067] Therefore, considering the situation of the local trend degree in time series, in the embodiment of the present invention, according to the deviation degree of the local trend degree in time series at each stable reference point, the available reference points are screened, including:

[0068] Considering that BIM water conservancy monitoring data is an incremental backup after a full backup, more attention should be paid to the newly added water conservancy monitoring data in the most recent period of time at each moment, so the trend analysis closer to each moment is given greater reference. For any sampling moment, each sampling moment before the sampling moment is used as the reference moment of the sampling moment, and the time difference between the sampling moment and each reference moment is negatively correlated and normalized to obtain the time weight of each reference moment. The smaller the time difference, the greater the consideration ratio.

[0069] For any stable reference point, the local trend degree of the stable reference point at the reference time is weighted based on the time weight and the average is calculated to obtain the available index of the stable reference point. The local trend degree is weighted by the time weight to calculate the concentration of the local trend degree. The larger the available index is, the better the concentration trend is.

[0070] Therefore, a stable reference point whose usable index is greater than a preset usable threshold is used as an usable reference point. In the embodiment of the present invention, the preset usable threshold is set to 0.5. The specific value can be adjusted by the implementer and is not limited here.

[0071] At this point, the screening analysis of the benchmark points with preliminary interpolation representatives is completed.

[0072] S3: At each sampling moment, the distribution range of each available benchmark point in the local available benchmark point within the regional range is calculated to obtain the regional distribution density index of each available benchmark point; the initial benchmark point is determined from the available benchmark points based on the regional distribution density index; the parameter value changes between the available benchmark points are analyzed by extending the initial benchmark point in different preset directions, and a new initial benchmark point is determined and iteratively extended until the new initial benchmark point cannot be determined.

[0073] Since the diffusion and conduction of various forces and substances are a relatively slow process, the number of benchmark points obtained by the above screening is still relatively large, which leads to an excessively high density of available benchmark points in some areas. In order to improve the efficiency of backup, we can further select fewer available benchmark points in high-density areas based on the properties of diffusion and conduction and the local density of many available monitoring points.

[0074] First, since the distribution of available benchmark points is not completely uniform, it is difficult to accurately evaluate the density by demarcating the area. Therefore, the regional range is determined by the local distribution range of each available benchmark point to evaluate the regional distribution density. Preferably, in the embodiment of the present invention, the method for obtaining the regional distribution density index can be referred to in Figure 2 , which shows a flow chart of a method for obtaining regional distribution density provided by an embodiment of the present invention, the method comprising the following steps:

[0075] S301: At each sampling time, for any available reference point, according to the distance distribution of other available reference points, obtain the regional distribution point of the available reference point.

[0076] By fixing and selecting the k available benchmark points with the closest local distance to the available benchmark points, the smaller the area used, the greater the density. Therefore, it is possible to consider determining the regional distribution points through distance distribution to analyze the regional scope.

[0077] In an embodiment of the present invention, the distance between each other available reference point and the available reference point is calculated, and all other available reference points are arranged in order of distance from small to large to obtain a range of available points sequence for the available reference point, which is arranged by distance size to facilitate screening out the available reference point with the closest distance.

[0078] Further, the first preset number of other available reference points in the range of available point sequence are used as regional distribution points of the available reference point. In an embodiment of the present invention, the preset number of areas is set to 8. When 8 are selected, the farther the distribution range is, the larger the occupied distribution area is.

[0079] S302: The maximum distance between the available reference point and the regional distribution point is used as the distribution radius of the available reference point; a circle is constructed with the available reference point as the center and the distribution radius as the radius, which is used as the regional range of the available reference point.

[0080] The regional range is determined by drawing a circle, with the distance between the farthest regional distribution points as the radius. The larger the area of ​​the regional range, the more it reflects that the distribution of locally available reference points is not concentrated.

[0081] S303: Obtain a regional distribution density index based on the number of regional distribution points of the available reference point and the size of the regional range.

[0082] In the form of a ratio, the ratio between the total number of regional distribution points of the available reference point and the area of ​​the regional range is used as the regional distribution density index of the available reference point. When the number of regional distribution points used for division is less and the area of ​​the obtained regional range is larger, the local distribution density is smaller. As an example, the expression of the regional distribution density index is:

[0083] ; In the formula, Expressed as The regional distribution density index of available benchmark points, Expressed as The total number of regional distribution points with available benchmarks, Expressed as The distribution radius of available reference points, Expressed as pi, Expressed as The area of ​​the region where the reference points are available.

[0084] The regional distribution density index can be used to further analyze the continuous differences in distribution changes of each available benchmark point, so that more representative benchmark points can be retained during reduction. First, the initial benchmark point is determined from the available benchmark points based on the regional distribution density index. In an embodiment of the present invention, at each sampling moment, the available benchmark point with the largest regional distribution density index is used as the initial benchmark point, and multi-directional changes are screened from the initial benchmark point with the highest density.

[0085] The distribution difference of available reference points is analyzed by extending the initial reference point in different directions, and only the reference points with large differences from the current initial reference point are retained, and the number of retained points is further reduced by iterative extension. Preferably, in an embodiment of the present invention, the parameter value changes between available reference points are analyzed by extending the initial reference point in different preset directions, a new initial reference point is determined, and iterative extension is performed until a new initial reference point cannot be determined, including:

[0086] First, the extension length of the initial benchmark point is obtained based on the regional distribution density index and distribution radius of the initial benchmark point. When screening similar situations, there may be more similar results for areas with higher density distribution, and a wider radius size range is retained when the analysis range is extended.

[0087] In an embodiment of the present invention, the method for obtaining the extension length includes:

[0088] The normalized value of the regional distribution density index of the initial benchmark point is used as the density influence coefficient of the initial benchmark point, and the regional range is analyzed and retained and adjusted with the distribution density as the weight value to improve the analysis efficiency. The product of the distribution radius of the initial benchmark point and the density influence coefficient is used as the extension length of the initial benchmark point, and the extension length is used to characterize the range of the extended analysis of each initial benchmark point.

[0089] Further extend in the preset extension direction with the initial reference point as the center, stop extending after reaching the extension length, and connect every two adjacent points where the extension is stopped in the preset extension direction to obtain each extension range. In an embodiment of the present invention, the preset extension direction is set to the eight-neighborhood direction, and each extension range is a triangular range composed of two extension lengths in adjacent directions and the line connecting the extension stop points. The implementer of the direction setting can adjust it according to the specific implementation scenario.

[0090] For any extended range, calculate the distance between each available reference point in the extended range and the initial reference point, and arrange the available reference points in order from small to large distance to obtain the extended reference point sequence of the extended range, and analyze similar situations from adjacent to far by distance.

[0091] Obtain the differential sequence of parameter values ​​of available reference points in the extended reference point sequence, and analyze the distribution differences of the parameter values. Traverse the differential sequence from front to back. When the absolute value of the differential value is greater than the preset differential threshold, it means that the deviation of the parameter value from near to far is large. The corresponding differential value is used as the stop differential value and the traversal is stopped, indicating that a new retention point can be determined to ensure the accuracy of data representation. In the embodiment of the present invention, the preset differential threshold is set to 0.3, and the specific numerical value implementer can adjust it according to the specific implementation scenario.

[0092] The available reference point corresponding to the largest serial number in the extended reference point sequence corresponding to the stop differential value is used as the new initial reference point on the extended range. All new initial reference points are iteratively extended to obtain new initial reference points until the new initial reference point cannot be determined, thus completing the selection of the reference point that can be used as the representation at each sampling moment.

[0093] S4: At each sampling moment, according to the proportion of each initial reference point as the initial reference point at all sampling moments, the final reference point at each sampling moment is screened out; based on the BIM water conservancy data to be backed up, the data at all final reference points and unstable reference points are compressed and stored.

[0094] Since the initial reference point is obtained by screening at a single moment, there may be an optimal situation in a single case, so it is necessary to further combine all sampling moments for evaluation to screen out a more representative final reference point. In an embodiment of the present invention, the method for obtaining the final reference point includes:

[0095] For any initial reference point at each sampling moment, the number of times the initial reference point is used as the initial reference point at all sampling moments is counted to obtain the total number of selections of the initial reference point, reflecting the representation ability of the initial reference point. The ratio of the total number of selections of the initial reference point to the total number of sampling moments is used as the screening index of the initial reference point. When the ratio is higher, it means that the representation ability of the initial reference point at all sampling moments is strong.

[0096] Therefore, the initial reference point whose screening index is greater than the preset screening threshold is used as the final reference point, so that the final reference point is completely retained to reduce storage pressure.

[0097] In an embodiment of the present invention, the data at all final reference points and unstable reference points in each hydraulic parameter of the BIM hydraulic data to be backed up need to be retained to a higher degree. The final reference point reflects the point with the strongest local characterization ability and needs to be fully retained to provide an interpolation basis for local monitoring point data. The information contained in the unstable reference point itself is more important and needs to be fully retained to provide a basis for subsequent abnormal analysis. Therefore, the data at the final reference point and the unstable reference point can be losslessly compressed, and the data of the remaining monitoring points can be losslessly compressed for storage. After the backup, the data of the basic points can greatly save storage space. Because of the spatial characteristics of the hydraulic engineering data, the corresponding compression method is obtained from the unique situation of the data between many basic points, and the corresponding data distribution can be inferred.

[0098] In summary, the present invention first analyzes the stability of water conservancy data in the time series of monitoring points, and preliminarily selects stable benchmark points. In water conservancy projects, due to the interaction between water flow and soil, various characteristics such as water quality or seepage will show a trend of gentle changes in space and time. Therefore, by reducing the monitoring points, the actual data can be inferred only by interpolating the necessary monitoring points, so the monitoring points with stability are reduced. Further analyze the consistency of stable benchmark points and local benchmark points at each moment, select benchmark points with better centralized stability, which can be used to characterize the local representative degree of data, and determine the more representative available benchmark points to reduce the monitoring points. At the same time, consider the aggregation of the available benchmark points selected in the regional distribution density, further reduce the representative monitoring points, and determine the final benchmark points. Finally, the unstable benchmark points and the reduced final benchmark points are combined for compressed storage. The present invention reduces the necessary storage number of monitoring points by conducting a comprehensive evaluation of the local time series and distribution density of the monitoring points with stable data, reduces the storage pressure, effectively increases the number of backups of BIM water conservancy data, and further improves the backup efficiency.

[0099] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A BIM-based water conservancy project monitoring data management method, characterized in that: The method comprises: For each hydraulic parameter of the BIM hydraulic data to be backed up, obtain the parameter values ​​of all monitoring points at each sampling time; According to the distribution stability of the parameter values ​​at each monitoring point at all sampling moments, the stable reference points with high stability are screened out; according to the distribution of the parameter values ​​between the stable reference points and the local monitoring points at each sampling moment, the local trend degree of each stable reference point at the sampling moment is obtained; according to the deviation degree of the local trend degree at each stable reference point in the time series, the stable reference points with high local trend degree distribution in the time series are determined as available reference points; At each sampling moment, the regional distribution density index of each available benchmark point is obtained according to the distribution of local available benchmark points within the regional range of each available benchmark point; the available benchmark point with the largest regional distribution density index is used as the initial benchmark point; the parameter value changes between the available benchmark points are analyzed by extending the initial benchmark point in different preset directions, and a new initial benchmark point is determined and iteratively extended until the new initial benchmark point cannot be determined; At each sampling moment, according to the proportion of each initial reference point as the initial reference point at all sampling moments, the initial reference point with a high proportion is selected as the final reference point; the data of all final reference points and unstable reference points in each water conservancy parameter of the BIM water conservancy data to be backed up are compressed and stored; The method for obtaining the local trend degree includes: Each stable reference point is taken as the target point in turn; within the preset local range of the target point, each other monitoring point is taken as a local point of the target point; the distance between each local point and the target point is negatively correlated and normalized to obtain the distance weight of each local point; At each sampling moment, the parameter values ​​of the local points are weighted and averaged based on the distance weight to obtain the local parameter value of the target point at each sampling moment; the difference between the parameter value of the target point and the local parameter value is negatively correlated and normalized to obtain the local trend degree of the target point at each sampling moment; The method for obtaining the regional distribution density index includes: At each sampling time, for any available reference point, according to the distance distribution of other available reference points, obtain a preset number of regional distribution points that are close to the available reference point; The maximum distance between the available reference point and the regional distribution point is used as the distribution radius of the available reference point; a circle is drawn with the available reference point as the center and the distribution radius as the radius, which is used as the regional range of the available reference point; The ratio between the total number of regional distribution points of the available benchmark point and the area of ​​the regional range is used as the regional distribution density index of the available benchmark point.

2. According to the BIM-based water conservancy project monitoring data management method of claim 1, it is characterized in that: The method for obtaining the stable reference point comprises: For any monitoring point, calculate the z-score of the parameter value at each sampling time at the monitoring point as the standard value at each sampling time at the monitoring point; The variance of the standard value at each sampling moment at the monitoring point is normalized by negative correlation mapping to obtain the time series stability index of the monitoring point; The monitoring point where the timing stability index is greater than or equal to the preset stability threshold is used as the stability reference point.

3. According to the BIM-based water conservancy project monitoring data management method of claim 1, it is characterized in that: The method for obtaining the available reference points includes: For any sampling moment, each sampling moment before the sampling moment is used as the reference moment of the sampling moment; the time difference between the sampling moment and each reference moment is negatively correlated and normalized to obtain the time weight of each reference moment; For any stable reference point, the local trend degree of the stable reference point at the reference time is weighted based on the time weight and the average value is calculated to obtain the available index of the stable reference point; A stable reference point whose usable index is greater than a preset usable threshold is regarded as a usable reference point.

4. According to the BIM-based water conservancy project monitoring data management method of claim 1, it is characterized in that: The method for acquiring the regional distribution points includes: Calculate the distance between each other available reference point and the available reference point, and arrange all other available reference points in ascending order of distance to obtain a range available point sequence of the available reference point; The first preset number of other available reference points in the range available point sequence are used as regional distribution points of the available reference point.

5. According to the BIM-based water conservancy project monitoring data management method of claim 1, it is characterized in that: The step of extending the initial reference point in different preset directions to analyze the parameter value changes between the available reference points, determining a new initial reference point, and iteratively extending until a new initial reference point cannot be determined includes: According to the regional distribution density index and distribution radius of the initial reference point, the extension length of the initial reference point is obtained; Extending in a preset extension direction with the initial reference point as the center, stopping the extension after reaching the extension length, and connecting every two adjacent points where the extension is stopped in the preset extension direction to obtain each extension range; For any extended range, the distance between each available reference point in the extended range and the initial reference point is calculated, and the available reference points are arranged in ascending order of distance to obtain an extended reference point sequence of the extended range; Obtain a differential sequence of parameter values ​​of available reference points in the extended reference point sequence; traverse the differential sequence from front to back, and when there is a differential value whose absolute value is greater than a preset differential threshold, use the corresponding differential value as a stop differential value and stop traversal, and use the available reference point corresponding to the largest serial number in the extended reference point sequence corresponding to the stop differential value as a new initial reference point on the extended range; All new initial reference points are iteratively extended to obtain new initial reference points until the new initial reference points cannot be determined.

6. According to the BIM-based water conservancy project monitoring data management method of claim 5, it is characterized in that: The method for obtaining the extension length includes: The value after normalization of the regional distribution density index of the initial benchmark point is used as the density influence coefficient of the initial benchmark point; The product of the distribution radius of the initial reference point and the density influence coefficient is taken as the extension length of the initial reference point.

7. The method for managing water conservancy project monitoring data based on BIM according to claim 1, characterized in that: The method for obtaining the final reference point includes: For any initial reference point at each sampling moment, count the number of times the initial reference point is used as the initial reference point at all sampling moments to obtain the total number of selections of the initial reference point; use the ratio of the total number of selections of the initial reference point to the total number of sampling moments as the screening index of the initial reference point; The initial reference point whose screening index is greater than the preset screening threshold is used as the final reference point.

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