Method and device for determining monitoring points in foundation pit, storage medium and program product

Abstract

The invention discloses a method and device for determining monitoring points in a foundation pit, a storage medium and a program product. The method comprises the steps that multiple collection moments are determined, monitoring data are collected at multiple monitoring points of a foundation pit at the collection moments, a monitoring data set corresponding to the monitoring points is obtained, and the monitoring data in the monitoring data set is used for at least representing the deformation amount of the foundation pit; constructing the plurality of monitoring data sets to obtain an initial evaluation decision matrix corresponding to the plurality of monitoring points; based on the initial evaluation decision matrix, determining a correlation degree between the monitoring point and a first standard value and a closeness degree between the monitoring point and a second standard value; based on the correlation degree and the close degree, evaluating the monitoring points to obtain evaluation data; and determining at least one target monitoring point to be monitored from the plurality of monitoring points based on the plurality of evaluation data corresponding to the plurality of monitoring points. The technical problem that the efficiency of determining the monitoring points of the foundation pit is low is solved.

Description

Technical Field

[0001] The present application relates to the field of foundation pit engineering, and in particular, to a method, device, storage medium and program product for determining monitoring points in a foundation pit. Background Art

[0002] At present, when the foundation pit tends to be stable in the later stage and there is no need for large-scale comprehensive monitoring, the monitoring points for subsequent monitoring are usually selected from the existing monitoring points by referring to the cumulative deformation amount, deformation rate and empirical method. However, the above method only compares by cumulative deformation amount or rate. During the monitoring, there may be accidental errors and gross errors in the deformation amount. Due to several errors, the monitoring values ​​are large, and there is a technical problem of low efficiency in determining the monitoring points of the foundation pit.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method, device, storage medium and program product for determining monitoring points in a foundation pit, so as to at least solve the technical problem of low efficiency in determining monitoring points in a foundation pit.

[0005] According to one aspect of an embodiment of the present application, a method for determining monitoring points in a foundation pit is provided, and the method may include: determining multiple collection times, and at the collection times, collecting monitoring data at multiple monitoring points in the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; constructing multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, determining the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and the closeness, evaluating the monitoring point to obtain evaluation data; based on the multiple evaluation data corresponding to the multiple monitoring points, determining at least one target monitoring point to be monitored from the multiple monitoring points.

[0006] Optionally, based on the initial evaluation decision matrix, the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value are determined, including: respectively determining the information entropy corresponding to multiple sub-elements in the initial evaluation decision matrix; converting the information entropy to obtain a difference coefficient, wherein the difference coefficient is used to characterize the effective amount of information corresponding to the sub-element; based on the difference coefficient, determining the weight data corresponding to the sub-element; based on the weight data, converting the initial evaluation decision matrix to obtain a first evaluation decision matrix; based on the first evaluation decision matrix, determining the correlation and closeness.

[0007] Optionally, based on the first evaluation decision matrix, determining the association and closeness includes: normalizing the first evaluation decision matrix to obtain a second evaluation decision matrix; standardizing the second evaluation decision data to obtain a target evaluation decision matrix; and determining the association and closeness based on the target evaluation decision matrix.

[0008] Optionally, determining the degree of association based on the target evaluation decision matrix includes: calling a pre-set first standard value; respectively determining the absolute differences between multiple sub-elements in the target evaluation decision matrix and the corresponding first standard values ​​to obtain multiple absolute differences; and determining the degree of association based on the multiple absolute differences.

[0009] Optionally, the second standard value includes a positive ideal solution and a negative ideal solution, and the closeness is determined based on the target evaluation decision matrix, including: determining a first distance between a sub-element in the target evaluation decision matrix and the positive ideal solution, and a second distance between a sub-element in the target evaluation decision matrix and the negative ideal solution; based on the first distance and the second distance, determining the sub-closeness corresponding to the sub-element in the target evaluation decision matrix; averaging multiple sub-closenesses corresponding to multiple sub-elements in the target evaluation decision matrix to obtain the closeness corresponding to the monitoring point.

[0010] Optionally, based on multiple evaluation data corresponding to multiple monitoring points, at least one target monitoring point to be monitored is determined from the multiple monitoring points, including: sorting the multiple monitoring points based on the evaluation data to obtain a sorting result; and determining the monitoring point that is ranked before the target in the sorting result as the target monitoring point.

[0011] According to another aspect of an embodiment of the present application, a device for determining monitoring points in a foundation pit is also provided, and the device may include: a processing unit, used to determine multiple collection times, and at the collection times, collect monitoring data at multiple monitoring points in the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; a construction unit, used to construct multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points; a first determination unit, used to determine, based on the initial evaluation decision matrix, the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix; an evaluation unit, used to evaluate the monitoring point based on the correlation and closeness to obtain evaluation data; a second determination unit, used to determine at least one target monitoring point to be monitored from multiple monitoring points based on multiple evaluation data corresponding to the multiple monitoring points.

[0012] According to another aspect of an embodiment of the present application, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for determining monitoring points in a foundation pit of an embodiment of the present application.

[0013] According to another aspect of an embodiment of the present application, a processor is also provided, which is used to run a program, wherein when the program is run by the processor, the method for determining monitoring points in a foundation pit of an embodiment of the present application is executed.

[0014] According to another aspect of an embodiment of the present application, a computer program product is also provided, which includes computer instructions, wherein when the computer instructions are executed by a processor, the method for determining monitoring points in a foundation pit of an embodiment of the present application is implemented.

[0015] In an embodiment of the present application, multiple collection times are determined, and at the collection times, monitoring data are collected at multiple monitoring points of the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; multiple monitoring data sets are constructed to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value are determined, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and the closeness, the monitoring point is evaluated to obtain evaluation data; based on the multiple evaluation data corresponding to the multiple monitoring points, at least one target monitoring point to be monitored is determined from the multiple monitoring points. That is to say, in an embodiment of the present application, a monitoring data set corresponding to a plurality of monitoring points is obtained, and an initial evaluation decision matrix is ​​constructed based on the monitoring data set. Based on the initial evaluation decision matrix, the correlation between each monitoring point and the corresponding first standard value, and the closeness between each monitoring point and the corresponding second standard value are determined. Based on the correlation and closeness corresponding to the monitoring point, the evaluation data corresponding to the monitoring point is determined. Based on the evaluation data, the target monitoring point to be monitored among the plurality of monitoring points can be determined, thereby solving the technical problem of low efficiency in determining the monitoring points of the foundation pit and achieving the technical effect of improving the efficiency in determining the monitoring points of the foundation pit. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a flow chart of a method for determining monitoring points in a foundation pit according to an embodiment of the present application;

[0018] Figure 2 It is a flow chart of a method for screening foundation pit monitoring points based on TOPSIS-grey correlation analysis method according to an embodiment of the present application;

[0019] Figure 3 is a schematic diagram of data preprocessing according to an embodiment of the present application;

[0020] Figure 4 is a schematic diagram of a device for determining monitoring points in a foundation pit according to an embodiment of the present application;

[0021] Figure 5 is a structural block diagram of a computer terminal according to an embodiment of the present application;

[0022] Figure 6 It is a block diagram of an electronic device for a method for determining monitoring points in a foundation pit according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to an embodiment of the present application, an embodiment of a method for determining monitoring points in a foundation pit is provided. The steps shown in the flowchart of the accompanying drawing can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] In this embodiment, a method for determining monitoring points in a foundation pit is proposed. The method can obtain monitoring data sets corresponding to multiple monitoring points, and construct an initial evaluation decision matrix based on the monitoring data sets. Based on the initial evaluation decision matrix, the correlation between each monitoring point and the corresponding first standard value, and the closeness between each monitoring point and the corresponding second standard value are determined. Based on the correlation and closeness corresponding to the monitoring point, the evaluation data corresponding to the monitoring point is determined. Based on the evaluation data, the target monitoring point to be monitored among the multiple monitoring points can be determined, thereby solving the technical problem of low efficiency in determining the monitoring points of the foundation pit and achieving the technical effect of improving the efficiency of determining the monitoring points of the foundation pit.

[0027] Figure 1 1 is a flow chart of a method for determining monitoring points in a foundation pit according to an embodiment of the present application. Figure 1 As shown, the method may include the following steps:

[0028] Step S102, determining multiple collection moments, and collecting monitoring data at multiple monitoring points of the foundation pit at the collection moments to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit.

[0029] In the technical solution provided in the above step S102 of the present application, the above collection time may be a pre-set time. The above monitoring point may be a pre-set monitoring point, which may be located in the foundation pit or on the ground near the foundation pit, and the monitoring point may be an evaluation object. The above monitoring data may be foundation pit monitoring data, including but not limited to total station monitoring data, inclinometer data, soil pressure data, etc., and the type of monitoring data is not specifically limited here.

[0030] Optionally, different monitoring equipment may be deployed at different monitoring points. The monitoring equipment may be used to collect monitoring data of the monitoring point at different times, thereby obtaining a monitoring data set corresponding to the monitoring point. The monitoring data set may include monitoring data corresponding to the monitoring point at different times.

[0031] Optionally, the data collection and sorting module can collect foundation pit monitoring data according to the foundation pit monitoring plan. The foundation pit monitoring data includes but is not limited to total station monitoring data, inclinometer data, soil pressure data, etc. The monitoring data can also be collected and sorted to store the monitoring data in a standard format (for example, TXT format).

[0032] Step S104: construct multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points.

[0033] In the technical solution provided in the above step S104 of the present application, monitoring data sets corresponding to multiple monitoring points are obtained, and the multiple monitoring data sets are combined to obtain an initial evaluation decision matrix corresponding to the monitoring points. The above initial evaluation decision matrix can be used to integrate monitoring data.

[0034] Optionally, the initial evaluation decision matrix can be used to integrate multiple sets of data from different monitoring points at different times and under different conditions into a unified framework to facilitate subsequent data analysis and comparison. The initial decision matrix can be used to standardize data, eliminate data dimension and unit differences, and enable different types of monitoring data to be compared and analyzed under the same standard. Through the decision matrix, the relative importance of each monitoring indicator can be clarified, that is, a weight is assigned to each indicator, which helps to consider the impact of each indicator on the final decision during the comprehensive evaluation. The initial decision matrix simplifies the processing of complex data, allowing decision makers to understand and analyze the stability of monitoring points in an intuitive way, thereby assisting decision-making, such as screening and prioritization of monitoring points.

[0035] Optionally, after acquiring multiple monitoring data sets, the multiple monitoring data sets may be combined to obtain an initial evaluation decision matrix, which may be used to score the monitoring conditions of the monitoring points to determine the monitoring points to be monitored from the multiple monitoring points.

[0036] For example, a monitoring data set collected by multiple monitoring points is obtained, and the monitoring data set may include monitoring data collected by the collection point at different collection times. Each monitoring point can be used as an evaluation object, and an initial decision evaluation matrix can be constructed based on the time series.

[0037] Step S106, based on the initial evaluation decision matrix, determine the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix.

[0038] In the technical solution provided in the above step S106 of the present application, the standard monitoring data corresponding to the monitoring point can be set in advance to obtain the first standard value. Further, after the initial evaluation decision matrix is ​​constructed, the correlation between the monitoring point and the first standard value can be determined based on the initial evaluation decision matrix, and the correlation can be used to characterize the similarity or closeness between the evaluation object and the first standard value.

[0039] Optionally, the initial evaluation decision matrix may include monitoring data sets of multiple monitoring points, and each monitoring point may be used as an evaluation object to construct an initial evaluation decision matrix based on a time series. The first standard value may be an element in a reference sequence, or may be a preset value, and the accuracy of the data monitored by the monitoring point may be determined by determining the correlation between the element in the initial evaluation decision matrix and the first standard value.

[0040] Optionally, a reference data series is determined. The reference data series may include first standard values ​​corresponding to different monitoring points. The first standard value may be used as an ideal comparison standard. The reference data series may be formed with the optimal value (or the worst value) of each indicator. The reference series may be recorded as:

[0041] X′0=(x′0(1),x′0(2),…,x′0(j))

[0042] The above X'0 can be used to represent the reference sequence. The above x'0(1), x'0(2), ..., x'0(j) can be used to represent the first standard values ​​corresponding to different monitoring points.

[0043] Optionally, the above-mentioned correlation degree may be determined based on a grey correlation analysis module, and may be a correlation sequence or a grey correlation sequence.

[0044] Optionally, the second standard value may be determined based on the initial evaluation decision matrix, and may include a positive ideal solution and negative ideal solution For example, the positive ideal solution may be the maximum value in the monitoring data set, and the negative ideal solution may be the minimum value in the monitoring data set.

[0045] Optionally, based on the initial evaluation decision matrix, the closeness between each monitoring data corresponding to the monitoring point and the second standard value can be determined, so that the final closeness corresponding to the monitoring point can be obtained.

[0046] For example, the degree of closeness corresponding to the monitoring points can be determined by using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

[0047] Step S108, based on the relevance and proximity, the monitoring points are evaluated to obtain evaluation data.

[0048] In the technical solution provided in the above step S108 of the present application, the above evaluation data can be used to characterize the evaluation score corresponding to the monitoring point, and can be used to determine the accuracy of the monitoring data collected by the monitoring point.

[0049] Optionally, the evaluation data corresponding to the observation point can be determined based on the correlation and proximity.

[0050] For example, the correlation and closeness corresponding to each monitoring point are obtained, and the correlation and closeness can be transmitted to the comprehensive evaluation score, sorting and screening module. The module adds the grey correlation sequence and the relative closeness of TOPSIS of each evaluation object (that is, monitoring point) and calculates the average to obtain the final score (that is, evaluation data) of each evaluation object. The evaluation data corresponding to the monitoring point can be recorded as F i , can be calculated by the following formula:

[0051]

[0052] Among them, S i It can be used to characterize the closeness of the corresponding monitoring points. i It can be used to characterize the correlation degree corresponding to the monitoring points.

[0053] Step S110, based on a plurality of evaluation data corresponding to a plurality of monitoring points, determining at least one target monitoring point to be monitored from the plurality of monitoring points.

[0054] In the technical solution provided in the above step S110 of the present application, after obtaining the evaluation data, multiple monitoring points can be sorted based on the evaluation data corresponding to each monitoring point, so that the target monitoring point that can continue to be monitored can be determined from the multiple monitoring points.

[0055] For example, after obtaining the evaluation data, multiple monitoring points can be sorted according to the evaluation data. The higher the score, the better the monitoring point and the stronger its stability. The lower the score, the less stable the point is. It still needs to be monitored in the later stage of the project and can continue to be used as a later monitoring point.

[0056] Optionally, this embodiment may determine a monitoring point with a good evaluation result as a target monitoring point, or may determine a monitoring point with a poor evaluation result as a target monitoring point. It should be noted that the method of determining the target monitoring point here is only for illustration and is not specifically limited here.

[0057] Through the above-mentioned steps S102 and S110 of the present application, multiple collection times are determined, and at the collection times, monitoring data are collected at multiple monitoring points of the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; multiple monitoring data sets are constructed to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value are determined, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and closeness, the monitoring point is evaluated to obtain evaluation data; based on the multiple evaluation data corresponding to the multiple monitoring points, at least one target monitoring point to be monitored is determined from the multiple monitoring points. That is to say, in an embodiment of the present application, a monitoring data set corresponding to a plurality of monitoring points is obtained, and an initial evaluation decision matrix is ​​constructed based on the monitoring data set. Based on the initial evaluation decision matrix, the correlation between each monitoring point and the corresponding first standard value, and the closeness between each monitoring point and the corresponding second standard value are determined. Based on the correlation and closeness corresponding to the monitoring point, the evaluation data corresponding to the monitoring point is determined. Based on the evaluation data, the target monitoring point to be monitored among the plurality of monitoring points can be determined, thereby solving the technical problem of low efficiency in determining the monitoring points of the foundation pit and achieving the technical effect of improving the efficiency in determining the monitoring points of the foundation pit.

[0058] The above method of this embodiment is further introduced below.

[0059] As an optional implementation, step S106, based on the initial evaluation decision matrix, determines the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, including: respectively determining the information entropy corresponding to multiple sub-elements in the initial evaluation decision matrix; converting the information entropy to obtain a difference coefficient, wherein the difference coefficient is used to characterize the effective amount of information corresponding to the sub-element; based on the difference coefficient, determining the weight data corresponding to the sub-element; based on the weight data, converting the initial evaluation decision matrix to obtain a first evaluation decision matrix; based on the first evaluation decision matrix, determining the correlation and closeness.

[0060] In this embodiment, after constructing the initial evaluation decision matrix, since different monitoring methods have different degrees of importance in monitoring and some monitoring points have less reference significance, the monitoring data can be weighted first to obtain a first evaluation decision matrix. Based on the first evaluation decision matrix, the correlation and closeness corresponding to the monitoring points can be determined.

[0061] Optionally, according to the monitoring implementation method, the weight corresponding to each monitoring data can be determined. In this embodiment, the entropy weight method can be adopted to calculate the weight ratio. The method may include the following steps: The information entropy corresponding to each monitoring data (that is, the sub-element in the initial evaluation decision matrix) can be calculated by the following steps:

[0062]

[0063] in, p ij It can be used to represent the weight of the i-th monitoring data on the j-th indicator. n can be used to represent the number of monitoring data in the monitoring data set. i can be used to represent the collection time corresponding to the sub-element. j can be used to represent the monitoring point corresponding to the sub-element.

[0064] Furthermore, the difference coefficient can be calculated, which can be used to reflect the effective information amount corresponding to the sub-element, and can be expressed by the following formula:

[0065] D j =1-E j

[0066] Optionally, based on the difference coefficient, weight data corresponding to the sub-element may be determined, and the weight corresponding to each sub-element may be calculated by the following formula:

[0067]

[0068] Among them, m can be used to represent the number of monitoring points.

[0069] Optionally, by multiplying the original sub-element with the corresponding weight data to obtain a new sub-element, the new sub-element can be used to construct the first evaluation decision matrix. Further, based on the first evaluation decision matrix, the correlation and closeness corresponding to the monitoring point can be determined.

[0070] As an optional implementation, based on the first evaluation decision matrix, determining the association and closeness includes: normalizing the first evaluation decision matrix to obtain a second evaluation decision matrix; standardizing the second evaluation decision data to obtain a target evaluation decision matrix; and determining the association and closeness based on the target evaluation decision matrix.

[0071] In this embodiment, since the monitoring data may be positive or negative, it is not convenient to compare. Therefore, after obtaining the first evaluation decision matrix, the sub-elements in the first evaluation decision matrix may be normalized to obtain the second evaluation decision matrix.

[0072] Optionally, the normalization process may include: using interval transformation to map the sub-elements in the first evaluation decision matrix to between 0 and 1, and the sub-elements in the first evaluation decision matrix may be normalized by the following formula:

[0073]

[0074] Among them, the above x i (k) can be used to characterize the sub-elements in the first evaluation decision matrix. It can be used to represent the minimum value among multiple sub-elements in the first evaluation decision matrix. It can be used to represent the maximum value among multiple sub-elements in the first evaluation decision matrix.

[0075] Optionally, the units of the monitoring data collected by different monitoring devices may be inconsistent. When the units of several parameters are inconsistent, standardization can be used to remove the dimensions, thereby completing the data preprocessing to construct the final evaluation decision matrix (that is, the target evaluation decision matrix). The normalized sub-elements can be standardized by the following formula:

[0076]

[0077] Among them, μ can be used to represent the mean of the monitoring data set corresponding to the normalized sub-element, and σ is the standard deviation of the monitoring data set.

[0078] Optionally, through normalization and standardization, the target evaluation decision matrix can be obtained:

[0079]

[0080] Each vertical column represents an adjusted monitoring data set corresponding to a monitoring point, and each element in the monitoring data set may be monitoring data obtained after weighted processing, normalized processing, and standardized processing.

[0081] As an optional implementation, determining the degree of association based on the target evaluation decision matrix includes: calling a pre-set first standard value; respectively determining the absolute differences between multiple sub-elements in the target evaluation decision matrix and the corresponding first standard values ​​to obtain multiple absolute differences; and determining the degree of association based on the multiple absolute differences.

[0082] In this embodiment, the first standard value may be preset, and the absolute differences between the multiple sub-elements in the target evaluation decision matrix and the corresponding first standard value may be determined to obtain multiple absolute differences, and the association degree may be determined based on the multiple absolute differences.

[0083] Optionally, the reference sequence may be pre-set as: X′0=(x′0(1), x′0(2), …, x′0(j)). Multiple sub-elements in the target evaluation decision matrix and the corresponding first standard values ​​may be used for calculation to calculate the absolute difference between each evaluated object indicator and the corresponding element of the reference sequence one by one, which may be calculated by the following formula:

[0084] |x′0(k)-x n (k)|(k=1,…,jn=1,…,i)

[0085] Among them, x n (k) can be used to characterize the sub-elements in the target evaluation decision matrix.

[0086] Furthermore, the correlation coefficient between the sub-element in the decision matrix and the corresponding element in the reference sequence can be evaluated based on the absolute difference, which can be expressed by the following formula:

[0087]

[0088] Among them, ρ can be used to represent the resolution coefficient, 0<ρ<1. The smaller ρ is, the greater the difference between the correlation coefficients is, and the stronger the discrimination ability is. For example, ρ can be 0.6.

[0089] Optionally, after determining the correlation coefficient corresponding to each sub-element, the mean of the correlation coefficient between the corresponding sub-element in the monitoring point and the corresponding element of the reference sequence can be calculated to reflect the correlation relationship between each monitoring point and the reference sequence, that is, the correlation order, so that the final grey correlation order score of each monitoring point can be obtained, which can be obtained by the following formula:

[0090]

[0091] For example, in the target evaluation decision matrix, the absolute differences between the multiple sub-elements corresponding to the monitoring point 1 and the corresponding first standard value are determined respectively, and the correlation coefficient corresponding to each sub-element is determined based on the absolute difference, and the mean of the relationship coefficients of the multiple sub-elements is determined to obtain the correlation degree corresponding to the monitoring point 1. The above steps can be used to determine the correlation degree corresponding to each monitoring point in the target evaluation decision matrix.

[0092] As an optional implementation, the second standard value includes a positive ideal solution and a negative ideal solution, and determines the closeness based on the target evaluation decision matrix, including: determining a first distance between a sub-element in the target evaluation decision matrix and the positive ideal solution, and a second distance between a sub-element in the target evaluation decision matrix and the negative ideal solution; determining the sub-closeness corresponding to the sub-element in the target evaluation decision matrix based on the first distance and the second distance; averaging multiple sub-closenesses corresponding to multiple sub-elements in the target evaluation decision matrix to obtain the closeness corresponding to the monitoring point.

[0093] In this embodiment, the first distance between the sub-element corresponding to the monitoring point in the target evaluation decision matrix and the positive ideal solution, and the second distance between the sub-element in the target evaluation decision matrix and the negative ideal solution can be determined. In the above manner, the first distance and the second distance corresponding to multiple sub-elements in the monitoring point can be obtained. Based on the first distance and the second distance of the sub-element, the sub-closeness corresponding to the sub-element can be determined, so that the sub-closeness corresponding to each sub-element can be obtained, and the mean of the multiple sub-closeness corresponding to the multiple sub-elements in the monitoring point can be determined, and the closeness corresponding to the monitoring point can be determined. This method can determine multiple closeness corresponding to multiple monitoring points based on the target evaluation decision matrix.

[0094] For example, the maximum value in the column of the target evaluation decision matrix can be determined as the positive ideal solution, and the minimum value in the column can be determined as the negative ideal solution, so that the positive ideal solution corresponding to each longitudinal fissure can be determined. and negative ideal solution The Euclidean distance from the sub-element in the target evaluation decision matrix to the positive ideal solution and the negative ideal solution can be calculated, and the Euclidean distance from the sub-element under each evaluation object to the positive ideal solution (that is, the first distance) and the Euclidean distance from the negative ideal solution (that is, the second distance) can be calculated by the following formula:

[0095]

[0096] in, It can be used to evaluate the first distance between sub-element i and the ideal solution. W can be the second distance from the evaluation sub-element i to the negative ideal solution. j It can be the weight of the evaluation indicator. and can be the positive ideal solution and negative ideal solution of the jth group respectively. ij It can be the value of sub-element i in the jth group.

[0097] Optionally, based on the first distance and the second distance, the relative proximity of the sub-elements can be calculated, which can be calculated by the following formula:

[0098]

[0099] Optionally, the relative closeness of multiple sub-elements may be averaged to obtain the closeness corresponding to the monitoring point.

[0100] As an optional implementation, step S110, based on multiple evaluation data corresponding to multiple monitoring points, determines at least one target monitoring point to be monitored from multiple monitoring points, including: sorting the multiple monitoring points based on the evaluation data to obtain a sorting result; and determining the monitoring point that is ranked before the target in the sorting result as the target monitoring point.

[0101] In this embodiment, based on the evaluation data, multiple monitoring points can be sorted to obtain a sorting result, and the monitoring point located before the target ranking can be set as the target monitoring point in advance. Therefore, the target monitoring point among the multiple monitoring points can be determined based on the sorting result. Among them, the above-mentioned target ranking can be a ranking set according to actual conditions. The sorting result can be from large to small, or from small to large, and can be selected according to actual conditions, and no specific restrictions are made here.

[0102] Table 1 Monitoring point score table

[0103] <![CDATA[Score of Monitoring Point 1 (F1)]]> 0.7434 <![CDATA[Score of Monitoring Point 2 (F2)]]> 0.7397 <![CDATA[Score of Monitoring Point 3 (F3)]]> 0.8399 <![CDATA[Score of Monitoring Point 4 (F4)]]> 0.8525 <![CDATA[Score of Monitoring Point 5 (F5)]]> 0.8338 <![CDATA[Score of Monitoring Point 6 (F6)]]> 0.7786 <![CDATA[Score of Monitoring Point 7 (F7)]]> 0.6207 <![CDATA[Score of Monitoring Point 8 (F8)]]> 0.6729

[0104] For example, by adding the grey correlation sequence and the relative closeness of TOPSIS for each evaluation object, averaging them, the final score of each evaluation object is calculated and sorted. The higher the score, the better the monitoring point and the stronger its stability. The lower the score, the poorer the stability of the point. It still needs to be monitored in the later stage of the project and can be used as a later monitoring point. For example, Table 1 is a monitoring point score table. As shown in Table 1, 8 monitoring points are designed, and the final results corresponding to each monitoring point can be: F1=0.7434, F2=0.7397, F3=0.8399, F4=0.8525, F5=0.8338, F6=0.7786, F7=0.6207, F8=0.6729. According to the sorting size, the most reliable point is F4 and the most unreliable point is F7. In the later monitoring, the points with poor reliability can be selected from these monitoring points according to the needs for the next stage of monitoring.

[0105] In an embodiment of the present application, a monitoring data set corresponding to a plurality of monitoring points is obtained, and an initial evaluation decision matrix is ​​constructed based on the monitoring data set. Based on the initial evaluation decision matrix, the correlation between each monitoring point and the corresponding first standard value and the closeness between each monitoring point and the corresponding second standard value are determined. Based on the correlation and closeness corresponding to the monitoring point, the evaluation data corresponding to the monitoring point is determined. Based on the evaluation data, the target monitoring point to be monitored among the plurality of monitoring points can be determined, thereby solving the technical problem of low efficiency in determining the monitoring points of the foundation pit and achieving the technical effect of improving the efficiency of determining the monitoring points of the foundation pit.

[0106] The above method is further described below with reference to another optional embodiment.

[0107] At present, in the analysis of foundation pit monitoring points, the stability of a monitoring point area is usually evaluated by the cumulative deformation of the monitoring point or its rate. This is a mature and effective method. However, in the later stage of the foundation pit, when the foundation pit tends to be stable and no large-scale comprehensive monitoring is required, how to select the monitoring points for subsequent monitoring from the existing monitoring points? The current method is to refer to the cumulative deformation, deformation rate and empirical method. However, the above method has the following problems: During the monitoring, some points may have large deformations at certain times, but small deformations in the later period. However, due to the large deformations in the previous few times, the cumulative deformation value is large, which will lead to evaluation errors; and there are many ways to monitor landslides, such as pressure monitoring, soil moisture monitoring, and rainfall monitoring. The units of their monitoring values ​​are different and cannot be compared together. It is impossible to evaluate the reliability of monitoring points, and ultimately it is impossible to provide a possibility for the selection of subsequent monitoring points; at the same time, relying on expert experience, there is a generally low efficiency, high requirements for expert experience, and it is easy to be affected by subjectivity.

[0108] To solve the above problems, in this embodiment, the foundation pit is monitored, and the monitoring data is processed by using the grey correlation analysis method combined with TOPSIS. In the data preprocessing, the monitoring values ​​of different measurement units are weighted through data standardization to solve the problem that they cannot be compared due to different units. After the data is processed by the grey correlation analysis method combined with TOPSIS, the final score of each monitoring point is calculated. By sorting the scores of the monitoring points, the reliability of each monitoring point can be judged, and a new method is provided for the subsequent selection of monitoring points.

[0109] Figure 2 is a flow chart of a method for screening foundation pit monitoring points based on TOPSIS-grey correlation analysis method according to an embodiment of the present application, such as Figure 2 As shown, the device may include the following parts: a data collection and sorting module 202; a data preprocessing module 204; a grey correlation analysis module 206; a TOPSIS module 208; and a comprehensive evaluation score, sorting and screening module 210.

[0110] Optionally, the data collection and sorting module 202 may transmit the collected monitoring data to the data preprocessing module 204 for data preprocessing. The data preprocessing module 204 may transmit the preprocessed data to the grey correlation analysis module 206 and the TOPSIS module 208 for data analysis. The TOPSIS module 208 and the grey correlation analysis module 206 may transmit the final calculation results (i.e., closeness and correlation) to the comprehensive evaluation score, sorting and screening module 210 to obtain the final score (i.e., evaluation data) of each monitoring point, and perform sorting and selection to determine the target monitoring point to be monitored from multiple monitoring points.

[0111] In this embodiment, the data collection and sorting module can collect and sort the data of different monitoring points according to the foundation pit monitoring plan. The data preprocessing module can construct an initial evaluation decision matrix based on the collected monitoring data, and perform positive, normalization, weighting and standardization processing on the initial evaluation decision matrix to obtain a target evaluation decision matrix. The TOPSIS module can include calculating positive ideal solutions and negative ideal solutions, which can be used to calculate the Euclidean distance from the sub-element in the target evaluation decision matrix to the positive ideal solution and the negative ideal solution, so that the closeness corresponding to the monitoring point can be determined by the Euclidean distance.

[0112] like Figure 2 As shown, the TOPSIS module determines the relative closeness and may include the following steps:

[0113] S2010, determining a positive ideal solution and a negative ideal solution.

[0114] In this embodiment, the positive ideal solution and the negative ideal solution may be the optimal solution and the worst solution for each group of indicators, respectively. Therefore, based on the target evaluation decision matrix, the positive ideal solution and the negative ideal solution corresponding to each indicator may be determined.

[0115] Step S2012, calculating the Euclidean distance.

[0116] In this embodiment, the Euclidean distance of each indicator from the positive ideal solution and the negative ideal solution of the group may be calculated.

[0117] Step S2014, calculating the relative closeness.

[0118] In this embodiment, the closeness of each comparison object can be calculated based on the Euclidean distance of each of the above-mentioned indicators.

[0119] like Figure 2 As shown, the grey correlation analysis module can determine the correlation degree through the following steps:

[0120] Step S22, determining a reference sequence.

[0121] In this embodiment, a preset reference number sequence may be obtained.

[0122] Optionally, a reference series may be selected as a desired comparison standard.

[0123] Step S24, calculating the absolute difference.

[0124] In this embodiment, based on the reference sequence, the absolute difference corresponding to each sub-element may be calculated.

[0125] Optionally, the absolute difference between the corresponding elements of each evaluated object indicator sequence (comparison sequence) and the reference sequence is calculated one by one.

[0126] Step S26, calculating the correlation coefficient.

[0127] In this embodiment, the correlation coefficient may be calculated, that is, the grey correlation coefficient between the reference number series and the comparison number series may be determined.

[0128] Step S28, determining the degree of association.

[0129] In this embodiment, the mean of the correlation coefficient between each evaluation object (comparison sequence) and the corresponding element of the reference sequence can be calculated to reflect the correlation relationship between each evaluation object and the reference sequence, which is called the correlation sequence.

[0130] In this embodiment, the comprehensive evaluation score and sorting and screening module can calculate the closeness of each evaluation indicator and the correlation coefficient of each evaluation indicator based on TOPSIS, comprehensively score and sort each evaluation indicator, and screen according to the number to be retained.

[0131] In this embodiment, the initial evaluation decision matrix is ​​preprocessed to obtain a target evaluation decision matrix.

[0132] Optionally, Figure 3 is a schematic diagram of data preprocessing according to an embodiment of the present application, such as Figure 3 As shown, data preprocessing can include weighting, normalization and standardization.

[0133] Optionally, an initial evaluation decision matrix is ​​constructed, and the initial evaluation decision matrix is ​​weighted, normalized and standardized to obtain a target evaluation decision matrix.

[0134] In this embodiment, in the grey correlation analysis, different units can be dimensionless to solve the problem that cannot be discussed due to different units; in monitoring, there is no need to worry about the problem that cannot be compared together, and monitoring points can be arranged according to local conditions to achieve better monitoring.

[0135] The traditional monitoring point selection method is to compare by accumulating deformation variables or rates, but in the monitoring, there will be accidental errors and gross errors in the deformation variables. Due to several errors, the monitoring value is large, which makes the accumulated deformation variable or rate large. It is a more arbitrary and rough method to select subsequent monitoring points, and it is not possible to accurately identify the stability of each monitoring point. In this embodiment, the data processing based on TOPSIS-grey correlation analysis method can solve the above problems and obtain the evaluation score of each point. Through sorting and comparison, the closer to 1, the better its stability, and the closer to 0, the poorer its reliability, which can be used as the object of subsequent observation. The above method can be used to analyze the evaluation data of each monitoring point. By sorting the evaluation data, their reliability can be known, and the accuracy of judgment can be improved. It should be noted that the size of the above numbers is only for example, and no specific restrictions are made here.

[0136] In this embodiment, a method for screening foundation pit monitoring points based on TOPSIS-grey correlation analysis method is provided, through which the reliability of each monitoring point for the foundation pit can be reflected, and the final score of each monitoring point (that is, evaluation data) is used as the standard for screening the monitoring points; through the data preprocessing module, dimensionless processing of the data is achieved to solve the problem of different units; through weighted adjustment, misjudgment of monitoring points with large gross errors can be avoided, and the weight of important monitoring methods can be increased, so as to achieve a more accurate analysis of the reliability of each monitoring point.

[0137] Optionally, compared with the single use of a certain method, the integrated use of TOPSIS and grey correlation analysis can avoid the inevitable defects in each method to improve the reliability of the results, thereby solving the technical problem of low efficiency in determining the monitoring points of the foundation pit and achieving the technical effect of improving the efficiency of determining the monitoring points of the foundation pit.

[0138] According to an embodiment of the present application, a device for determining monitoring points in a foundation pit is also provided. It should be noted that the device for determining monitoring points in a foundation pit of this embodiment can be used to execute the method for determining monitoring points in a foundation pit of the above embodiment of the present application.

[0139] Figure 4 Schematic diagram of a device for determining monitoring points in a foundation pit according to an embodiment of the present application. Figure 4 As shown, the device 40 for determining monitoring points in the foundation pit may include: a processing unit 402 , a construction unit 404 , a first determination unit 406 , an evaluation unit 408 and a second determination unit 410 .

[0140] The processing unit 402 is used to determine multiple collection moments, and at the collection moments, collect monitoring data at multiple monitoring points of the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit.

[0141] The construction unit 404 is used to construct multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points.

[0142] The first determination unit 406 is used to determine the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value based on the initial evaluation decision matrix, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix.

[0143] The evaluation unit 408 is used to evaluate the monitoring points based on the relevance and proximity to obtain evaluation data.

[0144] The second determining unit 410 is used to determine at least one target monitoring point to be monitored from the multiple monitoring points based on the multiple evaluation data corresponding to the multiple monitoring points.

[0145] The device for determining monitoring points in a foundation pit of this embodiment determines multiple collection moments through a processing unit, and collects monitoring data at multiple monitoring points in the foundation pit at the collection moments to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; constructs multiple monitoring data sets through a construction unit to obtain initial evaluation decision matrices corresponding to multiple monitoring points; determines the correlation between the monitoring point and the first standard value and the closeness between the monitoring point and the second standard value based on the initial evaluation decision matrix through a first determination unit, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix; evaluates the monitoring point based on the correlation and closeness through an evaluation unit to obtain evaluation data; determines at least one target monitoring point to be monitored from multiple monitoring points through a second determination unit based on multiple evaluation data corresponding to multiple monitoring points, thereby solving the technical problem of low efficiency in determining monitoring points of a foundation pit and achieving the technical effect of improving the efficiency of determining monitoring points of a foundation pit.

[0146] The embodiment of the present application may provide a computer terminal, which may be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced by a terminal device such as a mobile terminal.

[0147] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0148] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for determining monitoring points in the foundation pit: determine multiple collection times, and at the collection times, collect monitoring data at multiple monitoring points in the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; construct multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, determine the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and closeness, evaluate the monitoring point to obtain evaluation data; based on multiple evaluation data corresponding to multiple monitoring points, determine at least one target monitoring point to be monitored from multiple monitoring points.

[0149] Optionally, Figure 5 is a structural block diagram of a computer terminal according to an embodiment of the present application, such as Figure 5 As shown, the computer terminal 508 may include: one or more (only one is shown in the figure) processors 502 , a memory 504 and a transmission device 506 .

[0150] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining monitoring points in the foundation pit in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the above-mentioned method for determining monitoring points in the foundation pit is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal 508 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0151] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: determine multiple collection times, and at the collection times, collect monitoring data at multiple monitoring points of the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; construct multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, determine the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and closeness, evaluate the monitoring point to obtain evaluation data; based on the multiple evaluation data corresponding to the multiple monitoring points, determine at least one target monitoring point to be monitored from the multiple monitoring points.

[0152] It can be understood by those skilled in the art that Figure 5 The structure shown is for illustration only, and the computer terminal 508 may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID for short), a PAD, or other terminal devices. Figure 5 The structure of the computer terminal 508 is not limited. For example, the computer terminal 508 may also include Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 5 Different configurations shown.

[0153] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0154] According to an embodiment of the present application, a computer-readable storage medium is also provided, which includes a stored program, wherein the program executes the method for determining monitoring points in a foundation pit in the above embodiment.

[0155] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0156] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: determining multiple collection times, and at the collection times, collecting monitoring data at multiple monitoring points of the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; constructing multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, determining the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and closeness, evaluating the monitoring point to obtain evaluation data; based on multiple evaluation data corresponding to multiple monitoring points, determining at least one target monitoring point to be monitored from multiple monitoring points.

[0157] Optionally, the computer-readable storage medium can also execute the program code of the following steps: respectively determine the information entropy corresponding to multiple sub-elements in the initial evaluation decision matrix; convert the information entropy to obtain a difference coefficient, wherein the difference coefficient is used to characterize the effective amount of information corresponding to the sub-element; based on the difference coefficient, determine the weight data corresponding to the sub-element; based on the weight data, convert the initial evaluation decision matrix to obtain a first evaluation decision matrix; based on the first evaluation decision matrix, determine the correlation and closeness.

[0158] Optionally, the computer-readable storage medium may also execute program code for the following steps: normalizing the first evaluation decision matrix to obtain a second evaluation decision matrix; normalizing the second evaluation decision data to obtain a target evaluation decision matrix; and determining the degree of association and closeness based on the target evaluation decision matrix.

[0159] Optionally, the computer-readable storage medium may also execute program codes of the following steps: calling a pre-set first standard value; respectively determining the absolute differences between multiple sub-elements in the target evaluation decision matrix and the corresponding first standard values ​​to obtain multiple absolute differences; and determining the degree of association based on the multiple absolute differences.

[0160] Optionally, the computer-readable storage medium may also execute program code for the following steps: segmenting the text in the historical chapter to obtain a plurality of sub-texts; and constructing a historical feature vector set based on the occurrence frequency of the sub-texts.

[0161] Optionally, the computer-readable storage medium can also execute the program code of the following steps: determining a first distance between a sub-element in a target evaluation decision matrix and a positive ideal solution, and a second distance between a sub-element in a target evaluation decision matrix and a negative ideal solution; determining a sub-closeness corresponding to the sub-element in the target evaluation decision matrix based on the first distance and the second distance; averaging multiple sub-closenesses corresponding to multiple sub-elements in the target evaluation decision matrix to obtain the closeness corresponding to the monitoring point.

[0162] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: based on the evaluation data, sorting multiple monitoring points to obtain a sorting result; and determining the monitoring point that is located before the target ranking in the sorting result as the target monitoring point.

[0163] In this embodiment, monitoring data sets corresponding to multiple monitoring points are obtained, and an initial evaluation decision matrix is ​​constructed based on the monitoring data sets. Based on the initial evaluation decision matrix, the correlation between each monitoring point and the corresponding first standard value, and the closeness between each monitoring point and the corresponding second standard value are determined. Based on the correlation and closeness corresponding to the monitoring point, the evaluation data corresponding to the monitoring point is determined. Based on the evaluation data, the target monitoring point to be monitored among the multiple monitoring points can be determined, thereby solving the technical problem of low efficiency in determining the monitoring points of the foundation pit and achieving the technical effect of improving the efficiency of determining the monitoring points of the foundation pit.

[0164] According to an embodiment of the present application, a processor is also provided, which is used to run a program, wherein when the program is run by the processor, the method for determining monitoring points in the foundation pit in the above embodiment is executed.

[0165] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0166] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for determining monitoring points in the foundation pit: determine multiple collection times, and at the collection times, collect monitoring data at multiple monitoring points in the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; construct multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, determine the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and closeness, evaluate the monitoring point to obtain evaluation data; based on multiple evaluation data corresponding to multiple monitoring points, determine at least one target monitoring point to be monitored from multiple monitoring points.

[0167] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining monitoring points in the foundation pit in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method for determining monitoring points in the foundation pit. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0168] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: determine multiple collection times, and at the collection times, collect monitoring data at multiple monitoring points of the foundation pit to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation of the foundation pit; construct multiple monitoring data sets to obtain initial evaluation decision matrices corresponding to multiple monitoring points; based on the initial evaluation decision matrix, determine the correlation between the monitoring point and the first standard value, and the closeness between the monitoring point and the second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix, and the first standard value and the second standard value are different; based on the correlation and closeness, evaluate the monitoring point to obtain evaluation data; based on the multiple evaluation data corresponding to the multiple monitoring points, determine at least one target monitoring point to be monitored from the multiple monitoring points.

[0169] Optionally, the processor may also execute the program code of the following steps: respectively determine the information entropy corresponding to multiple sub-elements in the initial evaluation decision matrix; convert the information entropy to obtain a difference coefficient, wherein the difference coefficient is used to characterize the effective amount of information corresponding to the sub-element; based on the difference coefficient, determine the weight data corresponding to the sub-element; based on the weight data, convert the initial evaluation decision matrix to obtain a first evaluation decision matrix; based on the first evaluation decision matrix, determine the degree of association and closeness.

[0170] Optionally, the processor may also execute the following program code: normalize the first evaluation decision matrix to obtain a second evaluation decision matrix; normalize the second evaluation decision data to obtain a target evaluation decision matrix; and determine the correlation and closeness based on the target evaluation decision matrix.

[0171] Optionally, the processor may also execute the program code of the following steps: calling a preset first standard value; respectively determining the absolute differences between multiple sub-elements in the target evaluation decision matrix and the corresponding first standard value to obtain multiple absolute differences; and determining the degree of association based on the multiple absolute differences.

[0172] Optionally, the processor may also execute the program code of the following steps: determining a first distance between a sub-element in a target evaluation decision matrix and a positive ideal solution, and a second distance between a sub-element in a target evaluation decision matrix and a negative ideal solution; determining a sub-closeness corresponding to the sub-element in the target evaluation decision matrix based on the first distance and the second distance; averaging multiple sub-closenesses corresponding to multiple sub-elements in the target evaluation decision matrix to obtain the closeness corresponding to the monitoring point.

[0173] Optionally, the processor may also execute program codes of the following steps: sorting a plurality of monitoring points based on the evaluation data to obtain a sorting result; and determining a monitoring point that is before a target ranking in the sorting result as a target monitoring point.

[0174] By adopting the embodiment of the present application, a monitoring data set corresponding to multiple monitoring points is obtained, and an initial evaluation decision matrix is ​​constructed based on the monitoring data set. Based on the initial evaluation decision matrix, the correlation between each monitoring point and the corresponding first standard value and the closeness between each monitoring point and the corresponding second standard value are determined. Based on the correlation and closeness corresponding to the monitoring point, the evaluation data corresponding to the monitoring point is determined. Based on the evaluation data, the target monitoring point to be monitored among the multiple monitoring points can be determined, thereby solving the technical problem of low efficiency in determining the monitoring points of the foundation pit and achieving the technical effect of improving the efficiency of determining the monitoring points of the foundation pit.

[0175] According to an embodiment of the present application, a computer program product is also provided, which includes computer instructions, wherein when the computer instructions are executed by a processor, the method for determining monitoring points in a foundation pit in the above embodiment is implemented.

[0176] An embodiment of the present application may provide an electronic device, which may include a memory and a processor.

[0177] Figure 6 It is a block diagram of an electronic device for a method for determining monitoring points in a foundation pit according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0178] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 to a random access memory (RAM) 603. In RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0179] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0180] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a verification method for data. For example, in some embodiments, the verification method for data may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the verification method for data described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the data verification method in any other appropriate manner (for example, by means of firmware).

[0181] According to an embodiment of the present application, a method for determining monitoring points in a foundation pit is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0182] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0183] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0184] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0185] To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or an LCD (liquid crystal display, monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a path ball) through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0186] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.

[0187] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0188] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0189] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0191] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0192] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0193] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0194] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining monitoring points in a foundation pit, characterized in that: include: Determine a plurality of collection moments, and collect monitoring data at a plurality of monitoring points of the foundation pit at the collection moments to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation amount of the foundation pit; Constructing a plurality of the monitoring data sets to obtain an initial evaluation decision matrix corresponding to a plurality of the monitoring points; Based on the initial evaluation decision matrix, determining the degree of association between the monitoring point and a first standard value, and the degree of closeness between the monitoring point and a second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix; Based on the correlation degree and the proximity degree, the monitoring point is evaluated to obtain evaluation data; Based on the plurality of evaluation data corresponding to the plurality of monitoring points, at least one target monitoring point to be monitored is determined from the plurality of monitoring points.

2. The method according to claim 1, characterized in that The determining, based on the initial evaluation decision matrix, the degree of association between the monitoring point and the first standard value, and the degree of closeness between the monitoring point and the second standard value, comprises: Respectively determining the information entropy corresponding to the multiple sub-elements in the initial evaluation decision matrix; Converting the information entropy to obtain a difference coefficient, wherein the difference coefficient is used to characterize the effective information amount corresponding to the sub-element; Based on the difference coefficient, determining weight data corresponding to the sub-element; Converting the initial evaluation decision matrix based on the weight data to obtain a first evaluation decision matrix; Based on the first evaluation decision matrix, the degree of association and the degree of closeness are determined.

3. The method according to claim 2, characterized in that The determining the relevance and the closeness based on the first evaluation decision matrix includes: Normalizing the first evaluation decision matrix to obtain a second evaluation decision matrix; Standardizing the second evaluation decision data to obtain a target evaluation decision matrix; Based on the target evaluation decision matrix, the relevance and the closeness are determined.

4. The method according to claim 3, characterized in that The determining the degree of association based on the target evaluation decision matrix includes: Calling the first preset standard value; Respectively determine the absolute differences between a plurality of sub-elements in the target evaluation decision matrix and the corresponding first standard values ​​to obtain a plurality of the absolute differences; The degree of association is determined based on a plurality of the absolute differences.

5. The method according to claim 3, characterized in that: The second standard value includes a positive ideal solution and a negative ideal solution, and the determining the closeness based on the target evaluation decision matrix includes: Determining a first distance between a sub-element in the target evaluation decision matrix and the positive ideal solution, and a second distance between a sub-element in the target evaluation decision matrix and the negative ideal solution; Determining, based on the first distance and the second distance, a sub-closeness corresponding to a sub-element in the target evaluation decision matrix; The multiple sub-closenesses corresponding to the multiple sub-elements in the target evaluation decision matrix are averaged to obtain the closeness corresponding to the monitoring point.

6. The method according to claim 1, characterized in that The determining at least one target monitoring point to be monitored from the plurality of monitoring points based on the plurality of evaluation data corresponding to the plurality of monitoring points comprises: Based on the evaluation data, the plurality of monitoring points are sorted to obtain a sorting result; The monitoring point that is before the target ranking in the sorting result is determined as the target monitoring point.

7. A device for determining monitoring points in a foundation pit, characterized in that: include: A processing unit, configured to determine a plurality of collection moments, and collect monitoring data at a plurality of monitoring points of the foundation pit at the collection moments to obtain monitoring data sets corresponding to the monitoring points, wherein the monitoring data in the monitoring data sets are used to at least characterize the deformation amount of the foundation pit; A construction unit, used to construct the plurality of monitoring data sets to obtain an initial evaluation decision matrix corresponding to the plurality of monitoring points; A first determination unit is used to determine, based on the initial evaluation decision matrix, a correlation between the monitoring point and a first standard value, and a closeness between the monitoring point and a second standard value, wherein the first standard value is used to characterize the standard monitoring data corresponding to the monitoring point, and the second standard value is determined based on the initial evaluation decision matrix; An evaluation unit, configured to evaluate the monitoring point based on the correlation degree and the proximity degree to obtain evaluation data; The second determining unit is used to determine at least one target monitoring point to be monitored from the plurality of monitoring points based on the plurality of evaluation data corresponding to the plurality of monitoring points.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 6.

10. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when run by the processor.

Images