CN117875797A - Collaborative supervision method and system for construction engineering - Google Patents
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Abstract
The invention relates to the technical field of engineering supervision, and provides a construction engineering collaborative supervision method and system, comprising the following steps: obtaining the daily engineering quality score and workload of a construction project; acquiring a correlation regression coefficient of the workload; screening abnormal workload, obtaining a reference engineering quality score, and further obtaining the correlation abnormal change degree of the abnormal workload; obtaining abnormal dense approaching degree of the workload sequence according to the correlation abnormal change degree of the abnormal workload, the abnormal workload contained in the workload sequence and the workload, determining the self-adaptive autoregressive item number according to the abnormal dense approaching degree of the workload sequence, predicting future workload according to the self-adaptive autoregressive item number, finishing project progress assessment according to a prediction result, and realizing construction project collaborative supervision according to the project progress assessment. The invention solves the problem of poor quality of construction engineering supervision caused by independent supervision of different supervision works.
Description
Technical Field
The invention relates to the technical field of engineering supervision, in particular to a collaborative supervision method and system for construction engineering.
Background
The important content of the collaborative supervision of the construction engineering is the construction supervision, which requires supervision personnel to make detailed supervision plans and supervision implementation rules on the basis of carefully researching design files, design drawings and project actual investigation, and track and supervise the construction progress according to the supervision plans and supervision implementation rules, so that the engineering is ensured to be orderly carried out according to the plans, and delay are avoided. In the process of tracking the construction progress, future engineering progress is required to be predicted according to the completed engineering progress, whether and what problems occur in the engineering project are judged according to the difference between the prediction result and the engineering plan, corresponding preventive or solving measures are provided in the aspects of technology, organization, economy and the like, and smooth performance, controllable quality, safety and reliability of the engineering project are ensured.
At present, the supervision work of the engineering in the aspects of construction safety, construction quality, construction progress and the like is independently carried out, the relevance between the construction quality and the progress is ignored, the prediction result of the construction progress is inaccurate, the analysis and the judgment result of engineering projects are unreasonable, the construction arrangement cannot be adjusted in time, and the supervision quality of the construction engineering is affected.
Disclosure of Invention
The invention provides a collaborative supervision method and a collaborative supervision system for construction engineering, which are used for solving the problem of poor supervision quality of the construction engineering caused by independent supervision of different supervision works, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a collaborative supervision method for construction engineering, including the following steps:
obtaining the daily engineering quality score and workload of a construction project;
acquiring an engineering quality scoring sequence and a workload sequence, acquiring a workload estimation sequence according to the engineering quality scoring sequence and the workload sequence, and acquiring a correlation regression coefficient of each workload in the workload sequence according to the engineering quality scoring sequence, the workload sequence and the workload estimation sequence;
the method comprises the steps of obtaining workload screening abnormal workload contained in a workload sequence, obtaining an abnormal workload fluctuation vector, obtaining the chaotic degree of the abnormal workload, obtaining a reference engineering quality score fluctuation vector and the chaotic degree of a reference engineering quality score, and obtaining the correlation abnormal change degree of the abnormal workload according to the abnormal workload fluctuation vector, the reference engineering quality score fluctuation vector, the chaotic degree of the abnormal workload and the chaotic degree of the reference engineering quality score;
obtaining abnormal dense approaching degree of the workload sequence according to the correlation abnormal change degree of the abnormal workload, the abnormal workload contained in the workload sequence and the workload, determining the self-adaptive autoregressive item number according to the abnormal dense approaching degree of the workload sequence, predicting future workload according to the self-adaptive autoregressive item number, finishing project progress assessment according to a prediction result, and realizing construction project collaborative supervision according to the project progress assessment.
Further, the method for acquiring the engineering quality scoring sequence and the workload sequence comprises the following steps:
taking a preset day as a period, arranging daily engineering quality scores in the period according to time sequence, and obtaining an engineering quality score sequence of the period;
and arranging the daily workload in the period according to the time sequence, and obtaining a periodic workload sequence.
Further, the method for acquiring the workload estimation sequence according to the engineering quality scoring sequence and the workload sequence comprises the following steps:
taking the engineering quality score and the workload corresponding to the same day as a group of corresponding data, and performing linear fitting on the corresponding data of the engineering quality score sequence and the workload sequence to obtain a first fitting straight line;
acquiring a workload fitting value corresponding to each day in a period according to the first fitting straight line;
and arranging the fitting values of the workload according to the time sequence to obtain a workload estimation sequence.
Further, the method for acquiring the workload screening abnormal workload contained in the workload sequence comprises the following steps:
and performing anomaly detection on the workload sequence to obtain outlier factors of each workload in the workload sequence, and marking the workload corresponding to the outlier factors larger than a first anomaly threshold as the anomaly workload.
Further, the method for obtaining the chaotic degree of the abnormal workload comprises the following steps:
and recording the first preset threshold value of each of the front and back adjacent to the abnormal workload in the workload sequence as neighborhood data of the abnormal workload, and recording the information entropy of the neighborhood data of the abnormal workload as the chaotic degree of the abnormal workload.
Further, the method for obtaining the abnormal workload fluctuation vector comprises the following steps:
and arranging the chaotic degree of all abnormal workload in the workload sequence according to the time sequence, and obtaining the abnormal workload fluctuation vector.
Further, the method for obtaining the abnormal dense approaching degree of the workload sequence according to the correlation abnormal change degree of the abnormal workload, the abnormal workload contained in the workload sequence and the workload comprises the following steps:
the product of the correlation abnormal change degree of the abnormal workload and the average value of Euclidean distances between the abnormal workload and all the workload in the workload sequence is recorded as the abnormal concentration of the abnormal workload;
the sum of the abnormal densities of all abnormal workloads contained in the workload sequence is recorded as the abnormal dense approach degree of the workload sequence.
Further, the method for determining the self-adaptive autoregressive term number according to the abnormal dense approaching degree of the workload sequence comprises the following steps:
the downward rounded value of the product of the linear normalized value of the degree of outlier density approach of the workload sequence and the initial value of the autoregressive term number is recorded as the adaptive autoregressive term number.
Further, the method for predicting the future workload according to the number of the self-adaptive autoregressive items, finishing project progress evaluation according to the prediction result, and realizing collaborative supervision of the construction project according to the project progress evaluation comprises the following steps:
taking the self-adaptive autoregressive term number as the value of the autoregressive term number, and using an ARIMA autoregressive differential moving average model for the workload sequence to obtain the predicted value of the workload in the second preset threshold day in the future;
marking the sum of the predicted values of the workload on the second preset threshold day in the future as a first sum value, and marking the sum of the workload on the second preset threshold day in the future in the plan as a second sum value;
the ratio of the first sum value to the second sum value is recorded as the workload completion rate;
when the workload completion rate is smaller than a first comparison threshold value, considering the engineering progress lag, investigating the cause of the engineering progress lag, and adjusting personnel and building material configuration according to investigation results to ensure that the construction period is completed on time;
and when the workload completion rate is greater than or equal to a first comparison threshold value, the engineering progress is considered to be normal.
In a second aspect, an embodiment of the present invention further provides a collaborative monitoring system for construction engineering, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The beneficial effects of the invention are as follows:
according to the invention, a correlation regression coefficient is obtained according to the negative correlation relation between the engineering quality score and the workload by analyzing the correlation coefficient between the engineering quality score sequence and the workload sequence, and is used for evaluating the reliability of the correlation coefficient between the engineering quality score sequence and the workload sequence; then, the influence of the correlation between the engineering quality and the working progress on the abnormal judgment of the workload is analyzed, the correlation abnormal change degree is obtained according to the correlation between the abnormal workload fluctuation vector and the reference engineering quality score fluctuation vector and the change difference between the abnormal workload and the corresponding engineering quality score, and the accuracy of the abnormal judgment result of the workload is improved; when the density of the abnormal workload in the workload used for predicting the future workload is larger and the abnormality degree of the abnormal workload is more remarkable, the accuracy of the future workload predicted according to the abnormal workload is lower, so that the abnormal density approaching degree of the workload sequence is obtained according to the relativity abnormality change degree of the abnormal workload, the abnormal workload contained in the workload sequence and the workload, and the influence degree of the abnormal workload on the accuracy of the prediction result can be reflected by the abnormal density approaching degree; the method comprises the steps of determining the number of self-adaptive autoregressive terms according to the abnormal dense approaching degree of a workload sequence, predicting future workload according to the number of the self-adaptive autoregressive terms, finishing project progress assessment according to a prediction result, realizing collaborative supervision of construction projects, and predicting the future workload according to the number of the self-adaptive autoregressive terms, so that the accuracy of the ARIMA autoregressive differential moving average model on construction progress prediction can be improved, further, the construction arrangement can be timely adjusted according to the construction progress, the efficiency of supervision of the construction projects is improved, and the problem of poor quality of supervision of the construction projects caused by independent execution of different supervision works is solved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic flow chart of a collaborative supervision method for construction engineering according to an embodiment of the present invention;
FIG. 2 is a flow chart of outlier density approach acquisition.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a collaborative supervision method for construction projects according to an embodiment of the present invention is shown, and the method includes the following steps:
and S001, obtaining the daily engineering quality scores and workload of the construction engineering.
The building engineering mainly comprises foundation and foundation engineering, main structure engineering, roof engineering, decoration and fitment engineering, installation engineering and building energy-saving engineering, and is totally divided into six sub-modules.
And quantifying the engineering quality in the construction supervision process by a scoring method, scoring the daily construction content according to the current construction quality evaluation standard of the building engineering, and obtaining the score of each sub-module. Quantifying the engineering quality and scoring the construction content are known techniques, and will not be described in detail.
Because of the complexity of construction content, different weights need to be given to engineering quality scores of different work content. The foundation and foundation engineering weight, the main structure engineering weight, the roof engineering weight, the decoration and fitment engineering weight, the installation engineering weight and the building energy-saving engineering weight are respectively set as a first weight, a second weight, a third weight, a fourth weight, a fifth weight and a sixth weight. Wherein the empirical values of the first weight, the second weight, the third weight, the fourth weight, the fifth weight and the sixth weight are 0.1, 0.4, 0.05, 0.15, 0.2 and 0.1, respectively.
And carrying out weighted average on the scores of all the submodules every day according to the weights of the six submodules, and marking the obtained weighted average as the engineering quality score of the current day.
Whether the engineering progress is normal is mainly reflected as the stability degree of daily workload, if the predicted workload is obviously reduced compared with the daily workload, the engineering progress is slow, and the engineering progress needs to be adjusted in time. However, the complexity of the construction content is different every day, for example, when the main structure engineering is constructed, the complexity of the engineering is high, which results in a significant decrease in the workload, and at this time, the workload is reduced to a normal phenomenon. The staged construction period can be prolonged due to large engineering complexity, and the accuracy of the prediction result is not affected. Therefore, by giving higher weight to the construction module with high construction complexity, the construction quality score reflects the construction complexity of daily construction. Under normal construction conditions, the higher the engineering complexity, the higher the engineering quality score.
The daily workload is the ratio of the daily completed workload to the total workload of the construction engineering.
So far, the daily engineering quality score and workload of the construction engineering are obtained.
Step S002, obtaining engineering quality scoring sequence and workload sequence, obtaining workload estimation sequence according to engineering quality scoring sequence and workload sequence, obtaining correlation regression coefficient of each workload in workload sequence according to engineering quality scoring sequence, workload sequence and workload estimation sequence.
The slowing of the engineering progress caused by abnormal factors such as environment, material supply and the like has a great influence on the prediction of the construction progress, and the accuracy of the prediction result is reduced, so that the corresponding decision is not made by a supervisor. If the construction progress is slowed down due to abnormal factors such as environment, material supply and the like, the work load in a longer period of time needs to be comprehensively considered for the prediction of the construction progress, so that the accuracy of a prediction result is ensured.
In order to prevent the change of engineering complexity to workload from being mistakenly considered as abnormal engineering progress, according to the negative correlation between engineering quality scores and workload, taking 90 days as a period, and respectively arranging daily engineering quality scores and workload in the period according to time sequence to obtain an engineering quality score sequence and a workload sequence of the period.
The pearson correlation coefficient between the engineering quality score sequence and the workload sequence is obtained. The calculation of the pearson correlation coefficient is a known technology and will not be described in detail; the value range of the pearson correlation coefficient is more than or equal to-1 and less than or equal to 1.
And fitting the corresponding data of the engineering quality scoring sequence and the workload sequence by using a least square method and a straight line to obtain a first fitting straight line, and obtaining a workload fitting value corresponding to each day in one period according to the first fitting straight line. The method for performing linear fitting by using a least square method and obtaining a fitting value according to a fitting straight line are known techniques, and will not be described in detail.
And arranging the fitting values of the workload according to the time sequence to obtain a workload estimation sequence.
And obtaining a correlation regression coefficient of the workload of each day according to the difference between the workload estimation sequence and the workload sequence, wherein the correlation regression coefficient is used for evaluating the reliability of the correlation coefficient between the engineering quality scoring sequence and the workload sequence.
；
Wherein,is the>Personal workload->Correlation regression coefficients of (a); />Pearson correlation coefficients between the engineering quality scoring sequence and the workload sequence; />Is the>A work load;estimating the order of +.>Fitting the value of each workload; />For the first adjustment parameter, the empirical value is 1.
When the correlation coefficient is closer to-1, the negative correlation relationship between the engineering quality grading sequence and the workload sequence is more obvious, the correlation regression coefficient of the workload is larger, the reliability of the correlation coefficient between the engineering quality grading sequence and the workload sequence is higher, and the negative correlation relationship between the engineering quality and the workload is more obvious; when the difference between the workload and the workload fitting value is larger, the fitting value of the workload calculated through the first fitting straight line is inaccurate, the correlation regression coefficient of the workload is smaller, the reliability of the correlation coefficient between the engineering quality scoring sequence and the workload sequence is lower, and the positive correlation relation between the engineering quality and the workload is more remarkable.
So far, the correlation regression coefficients of all the workload are obtained.
Step S003, obtaining workload screening abnormal workload contained in the workload sequence, obtaining an abnormal workload fluctuation vector, obtaining the chaotic degree of the abnormal workload, obtaining a reference engineering quality score fluctuation vector and the chaotic degree of the reference engineering quality score, and obtaining the correlation abnormal change degree of the abnormal workload according to the abnormal workload fluctuation vector, the reference engineering quality score fluctuation vector, the chaotic degree of the abnormal workload and the chaotic degree of the reference engineering quality score.
The engineering quality score reflects the significance degree of the negative correlation between the engineering complexity and the workload, and when the engineering quality score is larger, the significance of the negative correlation between the change condition of the engineering complexity and the change condition of the workload is larger, namely the engineering is more complex, the engineering quality score is larger, and the corresponding workload is smaller. When the workload mutation caused by the environmental factors or the material supply problems occurs, a strong negative correlation between the workload variation and the engineering quality score variation can be influenced, so that the significance of the negative correlation between the engineering complexity variation and the workload variation is weakened, and at the moment, the workload in a short period of time is used for predicting the future workload, and the accuracy of the prediction result is lower.
Therefore, using a LOF anomaly detection algorithm on the workload sequence, a LOF outlier factor is obtained for each workload in the workload sequence. The empirical value of the neighborhood size k in the LOF anomaly detection algorithm is 8, and the use of the LOF anomaly detection algorithm to obtain the LOF outlier factor of each data in the sequence is a known technique and will not be described in detail. Setting a first abnormality thresholdWill be greater than the first abnormality threshold +.>The workload corresponding to the LOF outlier is marked as abnormal workload, wherein a first abnormal threshold +.>Is 0.6.
The abnormal workload and the front and the back of the abnormal workload in the workload sequence are respectively processedAnd marking the workload as neighborhood data of the abnormal workload, and acquiring information entropy of the neighborhood data of the abnormal workload. Wherein (1)>For the first preset threshold, the empirical value is 5. To be abnormally operatedThe information entropy of the neighborhood data is recorded as the chaotic degree of abnormal workload.
And marking the engineering quality scores corresponding to the abnormal workload in the engineering quality score sequence as reference engineering quality scores, and acquiring reference engineering quality score fluctuation vectors and the confusion degree of the reference engineering quality scores according to the method for acquiring the abnormal workload fluctuation vectors according to the abnormal workload.
And arranging the chaotic degree of all abnormal workload in the workload sequence according to the time sequence corresponding to the abnormal workload, and obtaining an abnormal workload fluctuation vector.
Each workload has a corresponding correlation regression coefficient, so each abnormal workload also has a corresponding correlation regression coefficient, and the correlation regression coefficient of the abnormal workload is the correlation regression coefficient of the corresponding workload in the workload sequence.
And acquiring cosine similarity between the abnormal workload fluctuation vector and the reference engineering quality score fluctuation vector, wherein when the absolute value of the cosine similarity is larger, the correlation between the abnormal workload fluctuation vector and the reference engineering quality score fluctuation vector is larger. When the cosine similarity is closer to-1, the correlation between the abnormal workload and the magnitude of change corresponding to the reference engineering quality score is smaller.
And obtaining the correlation abnormal change degree of the abnormal workload according to the abnormal workload fluctuation vector, the reference engineering quality score fluctuation vector, the confusion degree of the abnormal workload and the confusion degree of the reference engineering quality score.
；
Wherein,is->Abnormal workload->Degree of correlation abnormality variation of (2); />Is an abnormal workload->Correlation regression coefficients of (a); />Cosine similarity between the abnormal workload fluctuation vector and the engineering quality scoring fluctuation vector; />Is an abnormal workload->Is a degree of confusion of (2); />To and anomaly workload->Corresponding reference engineering quality score->Is (are) disarranged by>；/>For the first adjustment parameter, the empirical value is 1.
When the cosine similarity between the abnormal workload fluctuation vector and the engineering quality scoring vector is closer to-1 and the correlation regression coefficient of the abnormal workload is smaller, the correlation between the abnormal workload and the change amplitude corresponding to the engineering quality is smaller, the abnormal degree of the abnormal workload is more remarkable, namely the correlation abnormal change degree is larger.
Thus, the correlation abnormal change degree of all abnormal workload is obtained.
Step S004, obtaining abnormal dense approaching degree of the workload sequence according to the correlation abnormal change degree of the abnormal workload, the abnormal workload contained in the workload sequence and the workload, determining the self-adaptive autoregressive item number according to the abnormal dense approaching degree of the workload sequence, predicting the future workload according to the self-adaptive autoregressive item number, finishing project progress assessment according to the prediction result, and realizing construction project collaborative supervision according to the project progress assessment.
The number of abnormal workloads in the workload sequence is obtained.
The accuracy of the future workload predicted from the abnormal workload is lower when the density of the abnormal workload among the workloads for predicting the future workload is greater and the degree of abnormality of the abnormal workload is more remarkable. Therefore, the degree of abnormal dense approximation of the workload sequence is obtained from the degree of correlation abnormality change of the abnormal workload, the abnormal workload contained in the workload sequence, and the workload.
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Wherein,is the degree of abnormally dense approach to the workload sequence; />Is the>Abnormal workload->Degree of correlation abnormality variation of (2); />Is the number of abnormal workloads in the workload sequence; />Is the>Abnormal workload->The mean of the Euclidean distance to all of the workloads in the workload sequence.
When the distance between the abnormal workload and all the workloads in the workload sequence is larger and the degree of correlation abnormality change of the abnormal workload is larger, the degree of abnormality of the abnormal workload is higher, the influence on the accuracy of the prediction result is larger, and at the moment, the degree of abnormal dense approach of the workload sequence is larger.
Thus, the abnormal dense approach degree of the workload sequence is obtained, and the abnormal dense approach degree obtaining flow chart is shown in fig. 2.
And predicting the workload by using an ARIMA autoregressive differential moving average model so as to evaluate the engineering progress. The efficiency and accuracy of ARIMA autoregressive differential moving average model prediction are affected by the parameter of autoregressive term number, wherein the autoregressive term number is the number of historical observation values participating in prediction, and the larger the autoregressive term number is, the more accurate the prediction result is; the smaller the number of autoregressive terms, the higher the prediction efficiency.
In order to balance the prediction efficiency and the accuracy of the prediction result, the number of adaptive autoregressive terms is adaptively determined according to the degree of abnormal dense approach of the workload sequence.
；
Wherein,is the number of adaptive autoregressive terms; />The degree of abnormally dense approach to the workload sequence; />Is a downward rounding function; />Is a linear normalization function; />The empirical value was 8 for the initial value of the autoregressive term.
And predicting the workload of a second preset threshold day in the future by using an ARIMA autoregressive differential moving average model for the workload sequence by taking the self-adaptive autoregressive item number as the value of the parameter of the autoregressive item number. Wherein, the data prediction by using ARIMA autoregressive differential moving average model is a known technology and will not be described in detail; the empirical value of the parameter differential order of the ARIMA model is 2; the empirical value of the moving average order is 3; the empirical value of the second preset threshold is 10.
And (3) recording the ratio of the sum of the predicted workload of the future second preset threshold days to the sum of the workload of the future second preset threshold days in the plan as the workload completion rate.
When the workload completion rate is less than the first comparison thresholdWhen the construction process is considered to be lagged, the reasons of the lagged construction process are investigated, and personnel and building material configuration are adjusted according to investigation results, so that the construction period is ensured to be completed on time; when the work completion rate is greater than or equal to the first comparison threshold +.>And when the engineering progress is considered to be normal. Wherein the first comparison threshold +.>Is 0.8.
And so far, finishing project progress evaluation and realizing cooperative supervision of the construction project according to the project progress evaluation.
Based on the same inventive concept as the above method, the embodiment of the invention further provides a construction engineering collaborative supervision system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to realize the steps of any one of the above construction engineering collaborative supervision methods.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. The cooperative supervision method for the construction engineering is characterized by comprising the following steps of:
obtaining the daily engineering quality score and workload of a construction project;
acquiring an engineering quality scoring sequence and a workload sequence, acquiring a workload estimation sequence according to the engineering quality scoring sequence and the workload sequence, and acquiring a correlation regression coefficient of each workload in the workload sequence according to the engineering quality scoring sequence, the workload sequence and the workload estimation sequence;
the method comprises the steps of obtaining workload screening abnormal workload contained in a workload sequence, obtaining an abnormal workload fluctuation vector, obtaining the chaotic degree of the abnormal workload, obtaining a reference engineering quality score fluctuation vector and the chaotic degree of a reference engineering quality score, and obtaining the correlation abnormal change degree of the abnormal workload according to the abnormal workload fluctuation vector, the reference engineering quality score fluctuation vector, the chaotic degree of the abnormal workload and the chaotic degree of the reference engineering quality score;
obtaining abnormal dense approaching degree of the workload sequence according to the correlation abnormal change degree of the abnormal workload, the abnormal workload contained in the workload sequence and the workload, determining the self-adaptive autoregressive item number according to the abnormal dense approaching degree of the workload sequence, predicting future workload according to the self-adaptive autoregressive item number, finishing project progress assessment according to a prediction result, and realizing construction project collaborative supervision according to the project progress assessment.
2. The collaborative supervision method for construction projects according to claim 1, wherein the method for acquiring the project quality scoring sequence and the workload sequence comprises the following steps:
taking a preset day as a period, arranging daily engineering quality scores in the period according to time sequence, and obtaining an engineering quality score sequence of the period;
and arranging the daily workload in the period according to the time sequence, and obtaining a periodic workload sequence.
3. The collaborative supervision method for construction engineering according to claim 1, wherein the method for acquiring the workload estimation sequence according to the engineering quality scoring sequence and the workload sequence comprises the following steps:
taking the engineering quality score and the workload corresponding to the same day as a group of corresponding data, and performing linear fitting on the corresponding data of the engineering quality score sequence and the workload sequence to obtain a first fitting straight line;
acquiring a workload fitting value corresponding to each day in a period according to the first fitting straight line;
and arranging the fitting values of the workload according to the time sequence to obtain a workload estimation sequence.
4. The collaborative supervision method for construction engineering according to claim 1, wherein the method for acquiring workload screening abnormal workload included in the acquired workload sequence comprises the steps of:
and performing anomaly detection on the workload sequence to obtain outlier factors of each workload in the workload sequence, and marking the workload corresponding to the outlier factors larger than a first anomaly threshold as the anomaly workload.
5. The collaborative supervision method for construction engineering according to claim 1, wherein the method for obtaining the chaotic degree of abnormal workload is as follows:
and recording the first preset threshold value of each of the front and back adjacent to the abnormal workload in the workload sequence as neighborhood data of the abnormal workload, and recording the information entropy of the neighborhood data of the abnormal workload as the chaotic degree of the abnormal workload.
6. The collaborative supervision method for construction engineering according to claim 1, wherein the method for obtaining the abnormal workload fluctuation vector is as follows:
and arranging the chaotic degree of all abnormal workload in the workload sequence according to the time sequence, and obtaining the abnormal workload fluctuation vector.
7. The collaborative supervision method for construction engineering according to claim 1, wherein the method for obtaining the abnormal dense approaching degree of the workload sequence according to the correlation abnormal change degree of the abnormal workload, the abnormal workload contained in the workload sequence and the workload comprises the following steps:
the product of the correlation abnormal change degree of the abnormal workload and the average value of Euclidean distances between the abnormal workload and all the workload in the workload sequence is recorded as the abnormal concentration of the abnormal workload;
the sum of the abnormal densities of all abnormal workloads contained in the workload sequence is recorded as the abnormal dense approach degree of the workload sequence.
8. The collaborative supervision method for construction engineering according to claim 1, wherein the method for determining the number of adaptive autoregressive terms according to the degree of abnormal dense approach of the workload sequence is as follows:
the downward rounded value of the product of the linear normalized value of the degree of outlier density approach of the workload sequence and the initial value of the autoregressive term number is recorded as the adaptive autoregressive term number.
9. The method for collaborative supervision of construction projects according to claim 1, wherein the method for predicting future workload according to the number of adaptive autoregressive items, completing project progress assessment according to the prediction result, and implementing collaborative supervision of construction projects according to the project progress assessment is as follows:
taking the self-adaptive autoregressive term number as the value of the autoregressive term number, and using an ARIMA autoregressive differential moving average model for the workload sequence to obtain the predicted value of the workload in the second preset threshold day in the future;
marking the sum of the predicted values of the workload on the second preset threshold day in the future as a first sum value, and marking the sum of the workload on the second preset threshold day in the future in the plan as a second sum value;
the ratio of the first sum value to the second sum value is recorded as the workload completion rate;
when the workload completion rate is smaller than a first comparison threshold value, considering the engineering progress lag, investigating the cause of the engineering progress lag, and adjusting personnel and building material configuration according to investigation results to ensure that the construction period is completed on time;
and when the workload completion rate is greater than or equal to a first comparison threshold value, the engineering progress is considered to be normal.
10. A construction engineering collaborative supervision system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method according to any one of claims 1-9 when the computer program is executed by the processor.
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