Transformer substation construction progress monitoring method, device and equipment and storage medium

Abstract

The invention discloses a transformer substation construction progress monitoring method and device, equipment and a storage medium. The method comprises the steps of obtaining construction data of a to-be-constructed transformer substation; obtaining a current progress deviation value according to the current construction data; determining a first influence factor according to the planned construction data; according to the construction data in the preset prediction time period, the duration of the preset prediction time period and a preset completion progress prediction model, obtaining a prediction progress deviation value; performing principal component analysis on the predicted progress deviation value and the construction data, and determining a second influence factor; when the current progress deviation value is smaller than a first threshold value, a current deviation analysis report is generated, and early warning is carried out; when the prediction progress deviation value is smaller than a second threshold value, a prediction deviation analysis report is generated, and early warning is carried out. Through the implementation of the system and the method, the construction progress can be analyzed and pre-warned, the construction efficiency is improved, the project is ensured to be completed on schedule, meanwhile, the system depends on automatic data acquisition and analysis, and the labor cost and the management complexity are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering progress supervision, and in particular to a method, device, equipment and storage medium for monitoring the construction progress of a substation. Background Art

[0002] The 35kV substation is an important part of the power transmission and distribution system, and its construction process involves multiple links such as civil engineering, electrical equipment installation, and commissioning. During the construction of the substation, due to the numerous links and complex procedures, it is easy to have problems such as delayed progress and poor connection between various links, thus affecting the overall construction period and project quality.

[0003] Most existing monitoring methods rely on manual recording and reporting, lack analysis and early warning functions, and cannot meet the needs of modern power engineering construction. Summary of the invention

[0004] The present invention provides a substation construction progress monitoring and early warning method, device, equipment and storage medium, which can analyze and warn the current construction progress, thereby improving construction efficiency and ensuring that the project is completed on schedule. At the same time, this solution relies on automated data collection and analysis, reducing the need for manual intervention, reducing labor costs and management complexity.

[0005] An embodiment of the present invention provides a substation construction progress monitoring and early warning method, comprising:

[0006] Obtain the total workload of the substation to be constructed, the historical construction data of the current construction node, the current construction data and the planned construction data; wherein each of the above construction data contains several parameters, and the above parameters include: construction progress;

[0007] The current progress of the current construction node is calculated based on the current construction data and the total workload, and the current progress deviation of the current construction node is calculated based on the difference between the current progress and the first planned construction progress corresponding to the planned construction data;

[0008] Determine the correlation coefficient between the current progress deviation and the current construction data according to the planned construction data, the current construction data and the historical construction data, and use the parameter corresponding to the correlation coefficient that is not 0 as the first influencing factor corresponding to the current progress deviation;

[0009] According to the planned construction data within the preset prediction period, the current construction data, the duration of the preset prediction period and several preset completion progress prediction models, the predicted progress deviation within the preset prediction period is obtained;

[0010] Performing principal component analysis on the predicted progress deviation, historical construction data, planned construction data and current construction data, and selecting a parameter from the current construction data as a second influencing factor corresponding to the predicted progress deviation according to the result of the principal component analysis;

[0011] When the current progress deviation is less than the first threshold, generating a current deviation analysis report and issuing an early warning according to the current progress deviation and the first influencing factor;

[0012] When the predicted progress deviation is less than the second threshold, a prediction deviation analysis report is generated and an early warning is issued according to the predicted progress deviation and the second influencing factor.

[0013] Furthermore, the above-mentioned determination of the correlation coefficient between the above-mentioned current progress deviation amount and the above-mentioned current construction data according to the above-mentioned planned construction data, the current construction data and the historical construction data includes:

[0014] According to the Pearson correlation coefficient calculation formula, the planned construction data, the current construction data and the historical construction data, the correlation coefficient between each of the above parameters and the current progress deviation is calculated;

[0015] The above correlation coefficient is calculated according to the following formula:

[0016]

[0017] In the formula, r represents the above correlation coefficient, X i represents the i-th planned construction data, represents the mean of historical construction data, Y i represents the i-th current construction data, Represents the mean of current construction data.

[0018] Furthermore, the predicted progress deviation amount within the preset prediction period is obtained based on the planned construction data within the preset prediction period, the current construction data, the duration of the preset prediction period, and several preset completion progress prediction models, including:

[0019] The planned construction data, current construction data and the duration within the preset prediction period are sequentially input into each preset completion progress prediction model, so that each preset completion progress prediction model obtains the predicted construction completion progress corresponding to each preset completion progress prediction model according to the planned construction data, current construction data and the duration within the preset prediction period;

[0020] Based on the above-mentioned predicted construction completion progress and the planned construction data within the above-mentioned preset prediction period, the predicted progress deviation within the preset prediction period is calculated;

[0021] The predicted progress deviation within the above-mentioned preset prediction period is calculated according to the following formula:

[0022]

[0023] Where y represents the predicted progress deviation, W i represents the weight of the i-th preset completion progress prediction model, F i (x) represents the i-th predicted construction completion progress, and H0 represents the planned construction data within the preset prediction period.

[0024] Furthermore, the above-mentioned principal component analysis is performed on the above-mentioned predicted progress deviation, historical construction data, planned construction data and current construction data, and according to the result of the above-mentioned principal component analysis, a parameter is selected from the current construction data as the second influencing factor corresponding to the above-mentioned predicted progress deviation, including:

[0025] A covariance matrix is ​​constructed based on the predicted progress deviation, the historical construction data, the planned construction data and the current construction data;

[0026] Solving the above covariance matrix to obtain the eigenvalues ​​corresponding to the above parameters in the above covariance matrix;

[0027] The parameter corresponding to the largest eigenvalue is used as the second influencing factor.

[0028] Furthermore, it also includes:

[0029] When the current progress deviation is not less than the first threshold, generating a current deviation analysis report according to the current progress deviation and the first influencing factor;

[0030] When the predicted progress deviation is not less than the second threshold, a prediction deviation analysis report is generated according to the predicted progress deviation and the second influencing factor.

[0031] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0032] The present invention provides a substation construction progress monitoring device, comprising:

[0033] Data acquisition module, current progress deviation calculation module, first influencing factor determination module, predicted progress deviation prediction module, second influencing factor determination module, current deviation warning module and predicted deviation warning module;

[0034] The data acquisition module is used to obtain the total workload of the substation to be constructed, the historical construction data of the current construction node, the current construction data and the planned construction data; wherein each of the above construction data contains several parameters, and the above parameters include: construction progress;

[0035] The current progress deviation calculation module is used to calculate the current progress of the current construction node according to the current construction data and the total workload, and calculate the current progress deviation of the current construction node according to the difference between the current progress and the first planned construction progress corresponding to the planned construction data;

[0036] The first influencing factor determination module is used to determine the correlation coefficient between the current progress deviation and the current construction data according to the planned construction data, the current construction data and the historical construction data, and use the parameter corresponding to the correlation coefficient that is not 0 as the first influencing factor corresponding to the current progress deviation;

[0037] The above-mentioned prediction progress deviation amount prediction module is used to obtain the prediction progress deviation amount within the preset prediction period according to the planned construction data within the preset prediction period, the current construction data, the duration of the above-mentioned preset prediction period and several preset completion progress prediction models;

[0038] The second influencing factor determination module is used to perform principal component analysis on the predicted progress deviation, historical construction data, planned construction data and current construction data, and select a parameter from the current construction data as the second influencing factor corresponding to the predicted progress deviation according to the result of the principal component analysis;

[0039] The current deviation warning module is used to generate a current deviation analysis report and issue a warning according to the current progress deviation and the first influencing factor when the current progress deviation is less than a first threshold value;

[0040] The prediction deviation warning module is used to generate a prediction deviation analysis report and issue a warning based on the prediction progress deviation and the second influencing factor when the prediction progress deviation is less than a second threshold.

[0041] Furthermore, the above-mentioned prediction progress deviation prediction module includes:

[0042] Model prediction unit and prediction progress deviation calculation unit;

[0043] The above-mentioned model prediction unit is used to input the planned construction data, current construction data and the above-mentioned duration within the above-mentioned preset prediction period into each preset completion progress prediction model in sequence, so that each preset completion progress prediction model obtains the predicted construction completion progress corresponding to each preset completion progress prediction model according to the planned construction data, current construction data and the above-mentioned duration within the above-mentioned preset prediction period;

[0044] The predicted progress deviation calculation unit is used to calculate the predicted progress deviation within the preset prediction period according to the predicted construction completion progress and the planned construction data within the preset prediction period;

[0045] The predicted progress deviation within the above-mentioned preset prediction period is calculated according to the following formula:

[0046]

[0047] Where y represents the predicted progress deviation, W i represents the weight of the i-th preset completion progress prediction model, F i (x) represents the i-th predicted construction completion progress, and H0 represents the planned construction data within the preset prediction period.

[0048] Furthermore, it also includes: a current deviation analysis report generation module and a forecast deviation analysis report generation module;

[0049] The current deviation analysis report generating module is used to generate a current deviation analysis report according to the current progress deviation and the first influencing factor when the current progress deviation is not less than the first threshold;

[0050] The prediction deviation analysis includes a generation module, which is used to generate a prediction deviation analysis report according to the prediction progress deviation and the second influencing factor when the prediction progress deviation is not less than a second threshold.

[0051] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0052] The present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a substation construction progress monitoring method described in any embodiment of the present invention.

[0053] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0054] The present invention provides a storage medium, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a substation construction progress monitoring method according to any embodiment of the present invention is implemented.

[0055] The embodiments of the present invention have the following beneficial effects:

[0056] The present invention provides a method, device, equipment and storage medium for monitoring the construction progress of a substation. The method comprises: firstly obtaining the total workload of the substation to be constructed, historical construction data of the current construction node, current construction data and planned construction data; wherein each of the above construction data contains several parameters, and the above parameters include: construction progress; then, according to the above current construction data and the above total workload, the current progress of the current construction node is calculated, and according to the difference between the above current progress and the first planned construction progress corresponding to the above planned construction data, the current progress deviation of the current construction node is calculated; then, according to the above planned construction data, the current construction data and the historical construction data, the correlation coefficient between the above current progress deviation and the above current construction data is determined, and the parameter corresponding to the correlation coefficient that is not 0 is used as the above current progress deviation. The first influencing factor corresponding to the progress deviation; then, according to the planned construction data, current construction data, the duration of the above-preset prediction period and several preset completion progress prediction models within the preset prediction period, the predicted progress deviation within the preset prediction period is obtained; then, the above-predicted progress deviation, historical construction data, planned construction data and current construction data are subjected to principal component analysis, and according to the result of the above-mentioned principal component analysis, a parameter is selected from the current construction data as the second influencing factor corresponding to the above-mentioned predicted progress deviation; finally, when the above-mentioned current progress deviation is less than the first threshold, according to the above-mentioned current progress deviation and the above-mentioned first influencing factor, a current deviation analysis report is generated and an early warning is issued; when the above-mentioned predicted progress deviation is less than the second threshold, according to the above-mentioned predicted progress deviation and the above-mentioned second influencing factor, a predicted deviation analysis report is generated and an early warning is issued. Therefore, the present invention analyzes and warns the construction progress through the obtained current progress deviation, the first influencing factor, the predicted progress deviation confidence interval and the second influencing factor, thereby improving the construction efficiency and ensuring that the project is completed on schedule. At the same time, it relies on automated data collection and analysis, reduces the need for manual intervention, and reduces labor costs and management complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The present invention is a flowchart of a method for monitoring the progress of substation construction provided by an embodiment of the present invention.

[0058] Figure 2It is a structural schematic diagram of a substation construction progress monitoring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] like Figure 1 As shown, a method for monitoring the progress of substation construction provided by an embodiment of the present invention includes:

[0061] Step S101: Obtain the total workload of the substation to be constructed, the historical construction data of the current construction node, the current construction data and the planned construction data; wherein each of the above construction data contains several parameters, and the above parameters include: construction progress;

[0062] Specifically, the civil foundation construction of the substation mainly involves civil engineering work such as site preparation, foundation excavation, and concrete pouring; steel structure installation includes the installation of steel structures such as brackets and platforms to ensure the stability of the equipment; equipment installation involves the installation of core equipment such as transformers, circuit breakers, and switch cabinets; electrical wiring includes wiring all electrical equipment to ensure the connectivity of the electrical system; commissioning and acceptance include functional commissioning and final acceptance of the installed equipment.

[0063] Specifically, the entire construction process of the substation is divided into a number of construction nodes in advance, and planned construction data is preset at each construction node.

[0064] Specifically, the parameters in the construction data include: equipment installation completion, construction environment parameters, construction progress, and personnel attendance data; in the planned construction data, the "personnel attendance data" here corresponds to the personnel demand data. The equipment installation completion is the percentage of equipment installation corresponding to each construction node, such as a certain construction node requires 100% equipment installation completion; construction environment parameters are environmental conditions such as temperature, humidity, and wind speed; construction progress is the construction completion content, construction start time, construction duration, and construction end time corresponding to each construction node to preset the completion of the work content; the personnel demand data in the planned construction data is the preset number of construction personnel required for each construction node.

[0065] Specifically, the current construction data can be collected through sensors and cameras at the substation construction site. Among them, the attendance information of the construction personnel is collected through face recognition or attendance system to determine the construction personnel on site. Sensors include environmental sensors, position sensors and process sensors. Through the sensors and cameras installed on site, the construction situation is monitored in real time, such as the current soil moisture is 22%, the wind speed is 17 km / h, the temperature is 25°C, the civil foundation construction starts on September 1, 2021, the work duration is 1 week, the civil foundation construction is 80% complete, and there are currently 8 workers on site.

[0066] Step S102: Calculate the current progress of the current construction node according to the current construction data and the total workload, and calculate the current progress deviation of the current construction node according to the difference between the current progress and the first planned construction progress corresponding to the planned construction data;

[0067] Specifically, the current progress amount can be obtained according to the quotient of the current progress data in the current construction data and the total workload.

[0068] Schematically, the planned progress of a certain construction task is planned to be completed within 20 days, with a total workload of 100 units, and 5% of the workload is planned to be completed every day; the actual construction situation: 15 units of work were completed in the first 5 days, and 4 units of work were completed on the 6th day, then the planned completion percentage on the 6th day is 30%, the actual completion percentage is 19%, and the progress deviation is -11%, indicating that the actual progress lags behind the planned progress by 11%.

[0069] Step S103: determining the correlation coefficient between the current progress deviation and the current construction data according to the planned construction data, the current construction data and the historical construction data, and taking the parameter corresponding to the correlation coefficient that is not 0 as the first influencing factor corresponding to the current progress deviation;

[0070] In a preferred embodiment, the determination of the correlation coefficient between the current progress deviation and the current construction data based on the planned construction data, the current construction data and the historical construction data includes:

[0071] According to the Pearson correlation coefficient calculation formula, the planned construction data, the current construction data and the historical construction data, the correlation coefficient between each of the above parameters and the current progress deviation is calculated;

[0072] The above correlation coefficient is calculated according to the following formula:

[0073]

[0074] In the formula, r represents the above correlation coefficient, Xi represents the i-th planned construction data, represents the mean of historical construction data, Y i represents the i-th current construction data, Represents the mean of current construction data.

[0075] Specifically, the correlation coefficient r ranges from [-1,1]. When the value of the correlation coefficient is less than 0 but not equal to -1, it indicates that the parameter corresponding to the correlation coefficient is negatively correlated with the current progress deviation; when the value of the correlation coefficient is equal to -1, it indicates that the parameter corresponding to the correlation coefficient is completely negatively correlated with the current progress deviation; when the value of the correlation coefficient is equal to 0, it indicates that there is no correlation between the parameter corresponding to the correlation coefficient and the current progress deviation; when the value of the correlation coefficient is greater than 0 but not equal to 1, it indicates that the parameter corresponding to the correlation coefficient is positively correlated with the current progress deviation; when the value of the correlation coefficient is equal to 1, it indicates that the parameter corresponding to the correlation coefficient is completely positively correlated with the current progress deviation.

[0076] Specifically, after calculation, the results corresponding to the equipment installation completion degree, construction environment parameters and personnel attendance data in the parameters are shown to be positively correlated with the current progress deviation. Therefore, the first influencing factor is the equipment installation completion degree, construction environment parameters and personnel attendance data.

[0077] Indicatively, the information corresponding to the first influencing factor includes that there has been rainfall in recent days, which has affected external operations; key equipment has not been in place on time, affecting the progress of the process; and 5 workers are absent, affecting work efficiency.

[0078] In this preferred embodiment, a correlation analysis method is used to calculate the correlation coefficient of the planned construction data, the current construction data and the historical construction data, and the first influencing factor is determined.

[0079] Step S104: obtaining a predicted progress deviation within a preset prediction period according to the planned construction data within the preset prediction period, the current construction data, the duration of the preset prediction period, and a number of preset completion progress prediction models;

[0080] Preferably, the planned construction data within a preset forecast period and the current construction data are preprocessed, including data cleaning, processing of outliers, processing of missing values, processing of data noise and data standardization.

[0081] In a preferred embodiment, the predicted progress deviation amount within the preset prediction period is obtained based on the planned construction data within the preset prediction period, the current construction data, the duration of the preset prediction period, and several preset completion progress prediction models, including:

[0082] The planned construction data, current construction data and the duration within the preset prediction period are sequentially input into each preset completion progress prediction model, so that each preset completion progress prediction model obtains the predicted construction completion progress corresponding to each preset completion progress prediction model according to the planned construction data, current construction data and the duration within the preset prediction period;

[0083] Based on the above-mentioned predicted construction completion progress and the planned construction data within the above-mentioned preset prediction period, the predicted progress deviation within the preset prediction period is calculated;

[0084] The predicted progress deviation within the above-mentioned preset prediction period is calculated according to the following formula:

[0085]

[0086] Where y represents the predicted progress deviation, W i represents the weight of the i-th preset completion progress prediction model, F i (x) represents the i-th predicted construction completion progress, and H0 represents the planned construction data within the preset prediction period.

[0087] Preferably, the preset prediction period may be the next week, the next month, or the entire future substation construction period.

[0088] Specifically, the above-mentioned preset completion progress prediction models can be time series models, regression models, Bayesian models, and reinforcement learning models, etc. Time series models include autoregressive integrated moving average and long short-term memory networks, etc., and regression models include random forest regression, etc. Input historical construction data into each model, train each model, and verify the output results of each time on an independent data set to evaluate the prediction accuracy of the model, identify which models have performed well in past predictions, set the initial weight value of the model with good performance higher, and evaluate the performance of each model at different stages through cross-validation. According to the prediction accuracy and stability of the model, the weight of the model is dynamically adjusted. If a model performs poorly in the prediction of the last few construction nodes, the weight of the model will be reduced, and the parameters of other models will be adjusted to compensate.

[0089] Specifically, the preprocessed planned construction data within the preset prediction period, the current construction data, and the duration of the preset prediction period are input into each preset completion progress prediction model. Each preset completion progress prediction model will use the Monte Carlo simulation method to predict the percentage data of the predicted construction completion progress within the preset time.

[0090] Indicatively, if the preset completion progress prediction model is used now, the actual completion percentage on the 20th day of the entire future substation construction project cycle is obtained to be 95%, and the predicted progress deviation is -5%, which means that on the 20th day, it is expected to lag behind the planned progress by 5%.

[0091] In this preferred embodiment, the predicted progress deviation within the preset prediction period is obtained through the planned construction data within the preset prediction period, the current construction data, the duration of the above-mentioned preset prediction period and several preset completion progress prediction models.

[0092] Step S105: performing principal component analysis on the predicted progress deviation, historical construction data, planned construction data and current construction data, and selecting a parameter from the current construction data as a second influencing factor corresponding to the predicted progress deviation according to the result of the principal component analysis;

[0093] In a preferred embodiment, the above-mentioned predicted progress deviation, historical construction data, planned construction data and current construction data are subjected to principal component analysis, and according to the result of the above-mentioned principal component analysis, a parameter is selected from the current construction data as the second influencing factor corresponding to the above-mentioned predicted progress deviation, including:

[0094] A covariance matrix is ​​constructed based on the predicted progress deviation, the historical construction data, the planned construction data and the current construction data;

[0095] Solving the above covariance matrix to obtain the eigenvalues ​​corresponding to the above parameters in the above covariance matrix;

[0096] The parameter corresponding to the largest eigenvalue is used as the second influencing factor.

[0097] Specifically, the predicted progress deviation, historical construction data, planned construction data and current construction data are first integrated into a data matrix, and the relationship between the predicted progress deviation, the planned equipment installation completion degree corresponding to the preset prediction period corresponding to the predicted progress deviation, the construction environment parameters and the personnel demand data is analyzed to obtain the covariance matrix. The covariance matrix is ​​subjected to eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors, and the principal components are selected according to the size of the eigenvalues. The principal components represent the factors that affect the progress deviation.

[0098] Preferably, after obtaining the factors obtained by principal component analysis, causal inference methods can also be used, such as combining the significant causal factors obtained by using a statistical model with the main components obtained by principal component analysis to obtain more critical progress deviation prediction factor information.

[0099] In this preferred embodiment, by performing principal component analysis on the predicted progress deviation, historical construction data, planned construction data and current construction data, the second influencing factor corresponding to the predicted progress deviation is determined based on the results of the principal component analysis.

[0100] Step S106: when the current progress deviation is less than the first threshold, a current deviation analysis report is generated and an early warning is issued according to the current progress deviation and the first influencing factor;

[0101] Specifically, when the current progress deviation is less than the first threshold, an early warning is issued, and a current deviation analysis report is generated based on the current progress deviation and the first influencing factor, and the current deviation analysis report is sent to relevant staff via email.

[0102] Step S107: When the predicted progress deviation is less than the second threshold, a prediction deviation analysis report is generated and an early warning is issued according to the predicted progress deviation and the second influencing factor.

[0103] Specifically, when the predicted progress deviation is less than the second threshold, an early warning is issued, and a predicted deviation analysis report is generated based on the predicted progress deviation and the second influencing factor, and the predicted deviation analysis report is sent to relevant staff via email.

[0104] In a preferred embodiment, it also includes:

[0105] When the current progress deviation is not less than the first threshold, generating a current deviation analysis report according to the current progress deviation and the first influencing factor;

[0106] When the predicted progress deviation is not less than the second threshold, a prediction deviation analysis report is generated according to the predicted progress deviation and the second influencing factor.

[0107] Specifically, when the current progress deviation is not less than the first threshold, no warning is issued, and a current deviation analysis report is generated based on the current progress deviation and the first influencing factor, and the current deviation analysis report is sent to relevant staff via email; when the predicted progress deviation is not less than the second threshold, no warning is issued, and a predicted deviation analysis report is generated based on the predicted progress deviation and the second influencing factor, and the predicted deviation analysis report is sent to relevant staff via email.

[0108] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0109] like Figure 2 As shown, an embodiment of the present invention provides a substation construction progress monitoring device, comprising:

[0110] Data acquisition module, current progress deviation calculation module, first influencing factor determination module, predicted progress deviation prediction module, second influencing factor determination module, current deviation warning module and predicted deviation warning module;

[0111] The data acquisition module is used to obtain the total workload of the substation to be constructed, the historical construction data of the current construction node, the current construction data and the planned construction data; wherein each of the above construction data contains several parameters, and the above parameters include: construction progress;

[0112] The current progress deviation calculation module is used to calculate the current progress of the current construction node according to the current construction data and the total workload, and calculate the current progress deviation of the current construction node according to the difference between the current progress and the first planned construction progress corresponding to the planned construction data;

[0113] The first influencing factor determination module is used to determine the correlation coefficient between the current progress deviation and the current construction data according to the planned construction data, the current construction data and the historical construction data, and use the parameter corresponding to the correlation coefficient that is not 0 as the first influencing factor corresponding to the current progress deviation;

[0114] The above-mentioned prediction progress deviation amount prediction module is used to obtain the prediction progress deviation amount within the preset prediction period according to the planned construction data within the preset prediction period, the current construction data, the duration of the above-mentioned preset prediction period and several preset completion progress prediction models;

[0115] The second influencing factor determination module is used to perform principal component analysis on the predicted progress deviation, historical construction data, planned construction data and current construction data, and select a parameter from the current construction data as the second influencing factor corresponding to the predicted progress deviation according to the result of the principal component analysis;

[0116] The current deviation warning module is used to generate a current deviation analysis report and issue a warning according to the current progress deviation and the first influencing factor when the current progress deviation is less than a first threshold value;

[0117] The prediction deviation warning module is used to generate a prediction deviation analysis report and issue a warning based on the prediction progress deviation and the second influencing factor when the prediction progress deviation is less than a second threshold.

[0118] Furthermore, the above-mentioned prediction progress deviation prediction module includes:

[0119] Model prediction unit and prediction progress deviation calculation unit;

[0120] The above-mentioned model prediction unit is used to input the planned construction data, current construction data and the above-mentioned duration within the above-mentioned preset prediction period into each preset completion progress prediction model in sequence, so that each preset completion progress prediction model obtains the predicted construction completion progress corresponding to each preset completion progress prediction model according to the planned construction data, current construction data and the above-mentioned duration within the above-mentioned preset prediction period;

[0121] The predicted progress deviation calculation unit is used to calculate the predicted progress deviation within the preset prediction period according to the predicted construction completion progress and the planned construction data within the preset prediction period;

[0122] The predicted progress deviation within the above-mentioned preset prediction period is calculated according to the following formula:

[0123]

[0124] Where y represents the predicted progress deviation, W i represents the weight of the i-th preset completion progress prediction model, F i (x) represents the i-th predicted construction completion progress, and H0 represents the planned construction data within the preset prediction period.

[0125] Furthermore, it also includes: a current deviation analysis report generation module and a forecast deviation analysis report generation module;

[0126] The current deviation analysis report generating module is used to generate a current deviation analysis report according to the current progress deviation and the first influencing factor when the current progress deviation is not less than the first threshold;

[0127] The prediction deviation analysis includes a generation module, which is used to generate a prediction deviation analysis report according to the prediction progress deviation and the second influencing factor when the prediction progress deviation is not less than a second threshold.

[0128] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without creative work. The above schematic diagram is only an example of a substation construction progress monitoring device, and does not constitute a limitation on a substation construction progress monitoring device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0129] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0130] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a substation construction progress monitoring method described in any embodiment of the present invention.

[0131] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the device;

[0132] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor, a memory;

[0133] The processor referred to herein may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect various parts of the entire device;

[0134] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0135] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0136] Another embodiment of the present invention provides a storage medium, the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a substation construction progress monitoring method described above in any embodiment of the present invention.

[0137] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0138] Compared with the prior art, by implementing the above-mentioned embodiments of the present invention, the construction progress can be analyzed and warned, which improves construction efficiency and ensures that the project is completed on schedule. At the same time, it relies on automated data collection and analysis, reduces the need for manual intervention, and reduces labor costs and management complexity.

[0139] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for monitoring the progress of substation construction, characterized in that: include: Obtain the total workload of the substation to be constructed, the historical construction data of the current construction node, the current construction data and the planned construction data; wherein each of the construction data contains several parameters, and the parameters include: construction progress; The current progress amount of the current construction node is calculated based on the current construction data and the total workload, and the current progress deviation amount of the current construction node is calculated based on the difference between the current progress amount and the first planned construction progress corresponding to the planned construction data; Determine the correlation coefficient between the current progress deviation and the current construction data according to the planned construction data, the current construction data and the historical construction data, and use the parameter corresponding to the correlation coefficient that is not 0 as the first influencing factor corresponding to the current progress deviation; Obtaining a predicted progress deviation amount within a preset prediction period according to planned construction data within a preset prediction period, current construction data, the duration of the preset prediction period, and a number of preset completion progress prediction models; Performing principal component analysis on the predicted progress deviation, historical construction data, planned construction data, and current construction data, and selecting a parameter from the current construction data as a second influencing factor corresponding to the predicted progress deviation according to a result of the principal component analysis; When the current progress deviation is less than a first threshold, generating a current deviation analysis report and issuing an early warning according to the current progress deviation and the first influencing factor; When the predicted progress deviation is less than a second threshold, a prediction deviation analysis report is generated and an early warning is issued according to the predicted progress deviation and the second influencing factor.

2. A method for monitoring the progress of substation construction according to claim 1, characterized in that: Determining the correlation coefficient between the current progress deviation and the current construction data according to the planned construction data, the current construction data and the historical construction data includes: Calculate the correlation coefficient between each parameter and the current progress deviation according to the Pearson correlation coefficient calculation formula, the planned construction data, the current construction data and the historical construction data; The correlation coefficient is calculated according to the following formula: In the formula, r represents the correlation coefficient, X i represents the i-th planned construction data, represents the mean of historical construction data, Y i represents the i-th current construction data, Represents the mean of current construction data.

3. A method for monitoring the progress of substation construction according to claim 2, characterized in that: The method of obtaining the predicted progress deviation amount within the preset prediction period according to the planned construction data within the preset prediction period, the current construction data, the duration of the preset prediction period, and several preset completion progress prediction models includes: The planned construction data, current construction data and duration within the preset prediction period are sequentially input into each preset completion progress prediction model, so that each preset completion progress prediction model obtains the predicted construction completion progress corresponding to each preset completion progress prediction model according to the planned construction data, current construction data and duration within the preset prediction period; Calculate the predicted progress deviation within the preset prediction period based on the predicted construction completion progress and the planned construction data within the preset prediction period; The predicted progress deviation within the preset prediction period is calculated according to the following formula: Where y represents the predicted progress deviation, W i represents the weight of the i-th preset completion progress prediction model, F i (x) represents the i-th predicted construction completion progress, and H0 represents the planned construction data within the preset prediction period.

4. A method for monitoring the progress of substation construction according to claim 3, characterized in that: The performing of principal component analysis on the predicted progress deviation, historical construction data, planned construction data, and current construction data, and selecting a parameter from the current construction data as a second influencing factor corresponding to the predicted progress deviation according to the result of the principal component analysis, comprises: Constructing a covariance matrix according to the predicted progress deviation, the historical construction data, the planned construction data and the current construction data; Solving the covariance matrix to obtain the eigenvalues ​​corresponding to the parameters in the covariance matrix; The parameter corresponding to the largest eigenvalue is taken as the second influencing factor.

5. A method for monitoring the progress of substation construction according to claim 4, characterized in that: Also includes: When the current progress deviation is not less than a first threshold, generating a current deviation analysis report according to the current progress deviation and the first influencing factor; When the predicted progress deviation is not less than a second threshold, a predicted deviation analysis report is generated according to the predicted progress deviation and the second influencing factor.

6. A substation construction progress monitoring device, characterized in that: include: Data acquisition module, current progress deviation calculation module, first influencing factor determination module, predicted progress deviation prediction module, second influencing factor determination module, current deviation warning module and predicted deviation warning module; The data acquisition module is used to acquire the total workload of the substation to be constructed, the historical construction data of the current construction node, the current construction data and the planned construction data; wherein each of the construction data contains several parameters, and the parameters include: construction progress; The current progress deviation calculation module is used to calculate the current progress of the current construction node according to the current construction data and the total workload, and calculate the current progress deviation of the current construction node according to the difference between the current progress and the first planned construction progress corresponding to the planned construction data; The first influencing factor determination module is used to determine the correlation coefficient between the current progress deviation and the current construction data according to the planned construction data, the current construction data and the historical construction data, and use the parameter corresponding to the correlation coefficient that is not 0 as the first influencing factor corresponding to the current progress deviation; The predicted progress deviation amount prediction module is used to obtain the predicted progress deviation amount within the preset prediction period according to the planned construction data within the preset prediction period, the current construction data, the duration of the preset prediction period and several preset completion progress prediction models; The second influencing factor determination module is used to perform principal component analysis on the predicted progress deviation, historical construction data, planned construction data and current construction data, and select a parameter from the current construction data as the second influencing factor corresponding to the predicted progress deviation according to the result of the principal component analysis; The current deviation warning module is used to generate a current deviation analysis report and issue a warning according to the current progress deviation and the first influencing factor when the current progress deviation is less than a first threshold; The prediction deviation warning module is used to generate a prediction deviation analysis report and issue a warning according to the prediction progress deviation and the second influencing factor when the prediction progress deviation is less than a second threshold.

7. A substation construction progress monitoring device according to claim 6, characterized in that: The predicted progress deviation amount prediction module includes: Model prediction unit and prediction progress deviation calculation unit; The model prediction unit is used to input the planned construction data, current construction data and duration within the preset prediction period into each preset completion progress prediction model in sequence, so that each preset completion progress prediction model obtains the predicted construction completion progress corresponding to each preset completion progress prediction model according to the planned construction data, current construction data and duration within the preset prediction period; The predicted progress deviation calculation unit is used to calculate the predicted progress deviation within the preset prediction period according to the predicted construction completion progress and the planned construction data within the preset prediction period; The predicted progress deviation within the preset prediction period is calculated according to the following formula: Where y represents the predicted progress deviation, W i represents the weight of the i-th preset completion progress prediction model, F i (x) represents the i-th predicted construction completion progress, and H0 represents the planned construction data within the preset prediction period.

8. A substation construction progress monitoring device according to claim 7, characterized in that: It also includes: a current deviation analysis report generation module and a forecast deviation analysis report generation module; The current deviation analysis report generating module is used to generate a current deviation analysis report according to the current progress deviation and the first influencing factor when the current progress deviation is not less than a first threshold; The prediction deviation analysis includes a generation module, which is used to generate a prediction deviation analysis report according to the prediction progress deviation and the second influencing factor when the prediction progress deviation is not less than a second threshold.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for monitoring the progress of substation construction as described in any one of claims 1 to 5 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a substation construction progress monitoring method as described in any one of claims 1 to 5.

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