Building engineering supervision quality evaluation system based on big data technology

Abstract

The invention relates to the technical field of constructional engineering supervision, in particular to a constructional engineering supervision quality evaluation system based on a big data technology, which comprises a data collection module, a work anomaly analysis module, an environment anomaly analysis module and a comprehensive analysis module, wherein the data collection module collects working data of constructors, historical environment data and construction progress data; the work abnormity analysis module establishes a work efficiency curve and identifies a local minimum value point according to the construction personnel data, and determines a work efficiency abnormity time step; the environment anomaly analysis module calculates environment suitability degree scores and ranks the scores according to time steps to determine environment suitability degrees corresponding to the working efficiency anomaly time steps; and the comprehensive analysis module analyzes whether the abnormal time step is located at a local minimum point or not through the construction progress curve, judges the influence of environmental factors on the working efficiency according to environmental suitability statistics, and finally sends a result to a manager.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering supervision, and in particular to a construction engineering supervision quality assessment system based on big data technology. Background Art

[0002] Traditional construction project supervision quality assessment systems have technical problems in many aspects, specifically: first, these systems are usually unable to accurately correlate the work efficiency of construction personnel, environmental factors and construction progress, and lack multi-dimensional analysis capabilities based on a unified time step, resulting in limitations in the assessment of abnormal work efficiency;

[0003] Secondly, the traditional system's analysis of environmental factors is relatively superficial, lacking detailed calculation and ranking of specific environmental suitability, and unable to accurately assess the impact of the environment on work efficiency. This may cause managers to misjudge the reasons that affect construction efficiency, and then waste resources on irrelevant environmental regulation;

[0004] In addition, the evaluation process of traditional systems is often based on static or single data sources, lacking dynamic monitoring and real-time data processing capabilities. It is unable to promptly identify abnormal time steps in the construction progress, delaying the discovery and response of problems. The existence of these problems can easily lead to management decision-making errors, thereby affecting the efficiency optimization of construction projects and the rational allocation of resources. Summary of the invention

[0005] The purpose of the present invention is to provide a construction engineering supervision quality assessment system based on big data technology to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a construction engineering supervision quality assessment system based on big data technology, comprising a data collection module, a work abnormality analysis module, an environmental abnormality analysis module and a comprehensive analysis module, wherein:

[0007] The data collection module collects construction personnel work data, historical environmental data and construction progress data;

[0008] The work abnormality analysis module establishes a construction personnel work efficiency curve based on time steps according to the construction personnel work data, and determines the local minimum point in the curve as the work efficiency abnormality time step;

[0009] The environmental anomaly analysis module calculates the environmental suitability score of each time step in the historical environmental data; and sorts the historical environmental data in the order of the time step based on the time step and the environmental suitability score as the result; and selects the historical environmental data and the environmental suitability score corresponding to the work efficiency abnormal time step according to the sorting result as the work efficiency abnormal environmental data;

[0010] The comprehensive analysis module establishes a construction progress curve based on time steps according to the construction progress data; and selects the corresponding time step position of the construction progress curve according to the abnormal work efficiency time step to determine whether it is at the local minimum point position of the construction progress curve, wherein:

[0011] If it is at the local minimum point of the construction progress curve, analyze the abnormal working efficiency environment data and count the number of abnormal working efficiency environment data whose environmental suitability is lower than the preset environmental suitability threshold, where:

[0012] If this number is less than half of the number of time steps in the abnormal work efficiency time step, it means that the historical environmental data has no impact on the work efficiency of the construction personnel;

[0013] Otherwise, it means that the historical environmental data has an impact on the work efficiency of the construction personnel, and the historical environmental data with an environmental suitability lower than the preset environmental suitability threshold in the abnormal work efficiency environmental data is extracted and sent to the management personnel for processing.

[0014] As a further improvement of the present technical solution, the work data of the construction personnel in the data collection module includes working hours, completed tasks and task complexity; the historical environmental data includes temperature, humidity, wind speed and rainfall factors.

[0015] As a further improvement of the technical solution, the work abnormality analysis module establishes a construction personnel work efficiency curve based on a time step, and the process of determining the local minimum point in the curve specifically includes:

[0016] Define the calculation formula for the work efficiency of construction personnel, which is work efficiency = (amount of completed tasks * task complexity coefficient) / working hours;

[0017] The time step refers to the time interval at which the system records construction data, and the end time of each day is used as a unified time step unit;

[0018] The work efficiency of the corresponding construction personnel is calculated at each time step, and a construction personnel work efficiency curve based on the time step is drawn. The X-axis represents the time step, and the Y-axis represents the work efficiency of the construction personnel at the time step. The curve reflects the changing trend of the work efficiency of the construction personnel over time.

[0019] By detecting the local minimum points in the work efficiency curve, the decline in the work efficiency of the construction workers is determined. If the work efficiency of the construction workers at a certain time step is lower than the work efficiency of the construction workers at a time step in the field, the time step is determined to be a local minimum point and is used as an abnormal work efficiency time step.

[0020] As a further improvement of the technical solution, the environmental anomaly analysis module includes an environmental suitability calculation unit, and the process of the environmental suitability calculation unit calculating the environmental suitability score of each time step in the historical environmental data specifically includes:

[0021] Set the appropriate interval and corresponding weight of each environmental factor in the historical environmental data. The appropriate interval for temperature factor is set to 15℃-25℃; the appropriate interval for humidity factor is set to 30%-60%; the appropriate interval for wind speed factor is set to 0-10 m / s; the appropriate interval for rainfall factor is set to 0 mm;

[0022] By comparing with the corresponding suitable interval, the suitability sub-score of each environmental factor is calculated. For the temperature suitability sub-score, according to the degree of deviation between the temperature and the corresponding suitable interval, a linear function is used to convert the temperature into a score. When the temperature is between 15℃-25℃, the score is 1, when the temperature is higher than 35℃ or lower than 5℃, the score is 0, and in other cases, the score gradually decreases according to the degree of deviation; for the humidity suitability sub-score, when the humidity is in the range of 30%-60%, the score is 1, when it is higher or lower than this range, the score gradually decreases, and finally when it is lower than 20% or higher than 80%, the score is 0; for the wind speed suitability sub-score, when the wind speed is 0-10 m / s, the score is 1, when it exceeds 10 m / s, the score decreases, and when the wind speed is greater than 20 m / s, the score is 0; for the rainfall suitability sub-score, when there is no rainfall, the score is 1, and when there is rainfall, the score is 0;

[0023] Multiply each environmental factor suitability sub-score by its corresponding weight, and then add all weighted scores to obtain the environmental suitability score, where the weights are assigned according to the importance of different environmental factors to the impact of construction;

[0024] According to the above calculation process of environmental suitability score, the environmental suitability score is calculated for the environmental factors corresponding to each time step in the historical environmental data to obtain a value between 0 and 1.

[0025] As a further improvement of the technical solution, the abnormal environment analysis module includes an abnormal environment analysis unit, and the specific process of the abnormal environment analysis unit determining the abnormal environment data of work efficiency includes:

[0026] Using the same time step in the work abnormality analysis module, the historical environmental data is sorted in the order of the time step, and the environmental suitability scores are added to the sorted historical environmental data by time step to generate a final sorted list;

[0027] Compare the time steps of work efficiency anomalies calculated in the work anomaly analysis module with the time steps in the final sorted list, and select the historical environmental data and environmental suitability scores corresponding to the same time steps as the work efficiency anomaly environmental data.

[0028] As a further improvement of this technical solution, the process by which the comprehensive analysis module determines whether the time step in the work efficiency anomaly time step is at the local minimum point of the construction progress curve is as follows:

[0029] By recording the construction progress values at each time step t, draw a construction progress curve, with the X-axis representing the time step t and the Y-axis representing the construction progress P(t);

[0030] The local minimum point of the construction progress curve is defined as: if P(t) satisfies P(t) < P(t - 1) and P(t) < P(t + 1), then the time step t is the local minimum point;

[0031] Set the work efficiency anomaly time step as Ta, where Ta = {t1, t2,..., ta}. If P(ta) satisfies the definition of the local minimum point of the construction progress and the number of satisfied points is greater than half of the number of time steps in the work efficiency anomaly time step, it means that the time step position in the work efficiency anomaly time step Ta is at the local minimum point of the construction progress curve.

[0032] As a further improvement of this technical solution, the specific process by which the comprehensive analysis module analyzes the work efficiency anomaly environmental data is as follows:

[0033] For the work efficiency anomaly time step Ta, count the number of time steps Nlow in which the environmental suitability score S(ti) is lower than the preset environmental suitability threshold Sth. The formula is as follows:

[0034] where 1(S(ti) < Sth) means that at the time step ti, when S(ti) < Sth, the value is 1, otherwise the value is 0;

[0035] If It means that this number is less than half of the number of time steps in the work efficiency anomaly time step, indicating that the historical environmental data has no impact on the work efficiency of construction personnel;

[0036] Otherwise, it means that the historical environmental data has an impact on the work efficiency of construction personnel. Then extract the historical environmental data with environmental suitability lower than the preset environmental suitability threshold in the work efficiency anomaly environmental data and send it to the management personnel for processing.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. The construction engineering supervision quality assessment system based on big data technology can effectively exclude or confirm the impact of environmental factors on the work efficiency of construction personnel and the overall construction progress through a series of precise analysis steps; when the abnormal work efficiency time step is located at the local minimum point of the construction progress curve, it means that the construction progress is abnormal. In order to determine whether the environmental factors are the cause of the decline in work efficiency, the system will count and analyze the environmental suitability of these abnormal time steps; specifically, the system counts the number of time steps with environmental suitability lower than the preset threshold. If this number is less than half of the total number of abnormal time steps, the impact of the environment on work efficiency is excluded. The beneficial effect of this process is that it can effectively eliminate the interference of environmental factors on the construction progress, ensure that managers focus on dealing with construction efficiency issues that are not related to the environment, thereby optimizing resource allocation and construction management; and when environmental factors do affect efficiency, the system can identify them in time and send the corresponding data to managers, so that they can quickly take countermeasures to reduce delays and losses caused by adverse environmental conditions to the project progress. This method greatly improves the accuracy and efficiency of construction supervision quality and ensures the smooth progress of the project.

[0039] 2. The unified application of time steps in the construction engineering supervision quality assessment system based on big data technology is the core of the entire analysis process, which runs through the multi-dimensional analysis of work efficiency, environmental factors and construction progress; first, the system establishes a work efficiency curve based on time steps according to the work data of construction personnel, and determines the abnormal time steps of work efficiency by identifying the local minimum points therein; then, in the environmental factor analysis, the system calculates the environmental suitability score of each time step, and sorts the historical environmental data in time step order, and associates the abnormal time steps with their corresponding environmental data; finally, the system establishes a construction progress curve based on time steps according to the construction progress data, and determines whether the abnormal time step of work efficiency is located at the local minimum point of the construction progress; throughout the process, the time step is used as a unified time unit, so that the analysis of each module can be accurately matched, which not only improves the correlation between efficiency anomalies and environmental factors, but also effectively eliminates irrelevant factors, helping managers to quickly find the root cause of the problem and ensure the smooth progress of the construction project. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the overall module of the present invention;

[0041] Figure 2 It is a schematic diagram of the environmental anomaly analysis module unit of the present invention.

[0042] In the figure: 100, data collection module; 200, work abnormality analysis module; 300, environmental abnormality analysis module; 301, environmental suitability calculation unit; 302, abnormal environment analysis unit; 400, comprehensive analysis module. DETAILED DESCRIPTION

[0043] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments 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.

[0044] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0045] The following are some definitions of terms:

[0046] Abnormal work efficiency time step: refers to the time point in the work efficiency curve when the work efficiency of construction workers drops significantly. By detecting the local minimum point of the work efficiency curve, the system determines that the work efficiency is lower than the adjacent time steps of this time step, and marks this time step as an abnormal work efficiency time step;

[0047] Abnormal work efficiency environment data: refers to the historical environment data corresponding to the abnormal work efficiency time step. By matching the historical environment data of the abnormal work efficiency time step, the system can obtain the temperature, humidity, wind speed and other data during these time steps, and analyze the impact of environmental factors on work efficiency in combination with the environmental suitability score;

[0048] Environmental suitability score: refers to a comprehensive score calculated based on the suitable range of environmental factors such as temperature, humidity, wind speed and rainfall, which is used to evaluate the suitability of environmental conditions for the work efficiency of construction personnel in a specific time step. The score range is usually 0 to 1, and the higher the score, the more suitable the environment.

[0049] Next, see Figure 1-Figure 2 The present invention provides a technical solution: a construction engineering supervision quality assessment system based on big data technology, including a data collection module 100, a work abnormality analysis module 200, an environmental abnormality analysis module 300 and a comprehensive analysis module 400.

[0050] The data collection module 100 collects construction personnel work data, historical environment data and construction progress data, including:

[0051] The work data of construction personnel include working hours, completed tasks and task complexity, which are used to analyze the work efficiency of construction personnel. By drawing the work efficiency curve and identifying the possible abnormal time steps of work efficiency, understanding the status of construction personnel is helpful to find the potential reasons for delays in work progress;

[0052] Historical environmental data includes temperature, humidity, wind speed, and rainfall factors. Since environmental conditions have a direct impact on the work efficiency of construction personnel and the progress of the project, severe weather conditions may lead to reduced efficiency or suspension of construction. Therefore, analyzing environmental data helps determine whether environmental factors are the cause of abnormal work efficiency and progress.

[0053] Construction progress data is used to establish a construction progress curve to reflect the actual progress of the project, help identify whether there are delays or anomalies in the progress, and conduct correlation analysis with work efficiency and environmental data.

[0054] The work abnormality analysis module 200 establishes a work efficiency curve of the construction personnel based on the time step according to the work data of the construction personnel, and determines the local minimum point in the curve as the work efficiency abnormality time step, which specifically includes:

[0055] Define the calculation formula for the work efficiency of construction personnel, which is work efficiency = (amount of completed tasks * task complexity coefficient) / working hours;

[0056] The time step refers to the time interval at which the system records construction data. In order to ensure that different modules use the same time step and avoid deviation or error in analysis results due to inconsistent time steps, the end time of each day is used as a unified time step unit.

[0057] The work efficiency of the corresponding construction personnel is calculated at each time step, and a construction personnel work efficiency curve based on the time step is drawn. The X-axis represents the time step, and the Y-axis represents the work efficiency of the construction personnel at the time step. The curve reflects the changing trend of the work efficiency of the construction personnel over time.

[0058] By detecting the local minimum points in the work efficiency curve, the decline in the work efficiency of the construction workers is determined. If the work efficiency of the construction workers at a certain time step is lower than the work efficiency of the construction workers at a time step in the field, the time step is determined to be a local minimum point and is used as an abnormal work efficiency time step.

[0059] The environmental suitability calculation unit 301 in the environmental anomaly analysis module 300 calculates the environmental suitability score of each time step in the historical environmental data, specifically including:

[0060] Since the appropriate temperature range is very important for the work efficiency of construction workers, too high or too low temperature will affect work performance; extreme humidity (too high or too low) will increase the difficulty of construction, affect the operation of equipment and the comfort of personnel; high wind speed may threaten construction safety, causing construction to be suspended or reduce efficiency; rain may cause construction to be suspended or affect construction progress, especially outdoor work;

[0061] Therefore, the suitable interval and corresponding weight of each environmental factor in the historical environmental data are set. The suitable interval of the temperature factor is set to 15℃-25℃. If it is lower or higher than this range, the environmental suitability score will decrease; the suitable interval of the humidity factor is set to 30%-60%. If it is lower or higher than this range, the environmental suitability score will decrease; the suitable interval of the wind speed factor is set to 0-10 m / s. If the wind speed exceeds 10 m / s, the environmental suitability score will be greatly reduced; the suitable interval of the rainfall factor is set to 0 mm. Any rainfall will have a negative impact on the construction.

[0062] By comparing with the corresponding suitable interval, the suitability sub-score of each environmental factor is calculated. For the temperature suitability sub-score, according to the degree of deviation between the temperature and the corresponding suitable interval, a linear function is used to convert the temperature into a score. When the temperature is between 15℃-25℃, the score is 1, when the temperature is higher than 35℃ or lower than 5℃, the score is 0, and in other cases, the score gradually decreases according to the degree of deviation; for the humidity suitability sub-score, when the humidity is in the range of 30%-60%, the score is 1, when it is higher or lower than this range, the score gradually decreases, and finally when it is lower than 20% or higher than 80%, the score is 0; for the wind speed suitability sub-score, when the wind speed is 0-10 m / s, the score is 1, when it exceeds 10 m / s, the score decreases, and when the wind speed is greater than 20 m / s, the score is 0; for the rainfall suitability sub-score, when there is no rainfall, the score is 1, and when there is rainfall, the score is 0;

[0063] Multiply each environmental factor suitability sub-score by the corresponding weight, and then add up all weighted scores to get the environmental suitability score, where the weights are assigned according to the importance of different environmental factors to the construction, for example, the weight of the temperature factor is set at 40%, the humidity factor is 30%, the wind speed factor is 20%, and the rainfall factor is 10%. The sum of these weights should be 1;

[0064] According to the above calculation process of environmental suitability score, the environmental suitability score is calculated for the environmental factors corresponding to each time step in the historical environmental data to obtain a value between 0 and 1.

[0065] The abnormal environment analysis unit 302 in the environment anomaly analysis module 300 sorts the historical environment data in the order of time steps, with the environmental suitability score as the result; selects the historical environment data and the environmental suitability score corresponding to the time steps with abnormal work efficiency according to the sorting result as the environmental data with abnormal work efficiency, specifically including:

[0066] Using the same time steps in the work anomaly analysis module 200, sort the historical environment data in the order of time steps, and pair the environmental suitability scores with the sorted historical environment data according to the time steps to generate a final sorted list;

[0067] Compare the time steps with abnormal work efficiency calculated in the work anomaly analysis module 200 with the time steps in the final sorted list, and select the historical environment data and the environmental suitability score corresponding to the same time steps as the environmental data with abnormal work efficiency.

[0068] The comprehensive analysis module 400 establishes a construction progress curve based on time steps according to the construction progress data; and selects the corresponding time step position of the construction progress curve according to the time steps with abnormal work efficiency to determine whether it is at the position of the local minimum point of the construction progress curve, specifically including:

[0069] By recording the construction progress value at each time step t, draw a construction progress curve, with the X-axis representing the time step t and the Y-axis representing the construction progress P(t);

[0070] The local minimum point of the construction progress curve is defined as: if P(t) satisfies P(t) < P(t - 1) and P(t) < P(t + 1), then the time step t is the local minimum point;

[0071] Set the time steps with abnormal work efficiency as Ta, where Ta = {t1, t2,..., ta}. If P(ta) satisfies the definition of the local minimum point of the construction progress and the number of satisfied ones is greater than half of the number of time steps in the time steps with abnormal work efficiency, it means that the time step positions in the time steps with abnormal work efficiency Ta are at the local minimum point positions of the construction progress curve; where:

[0072] If the time step positions in the time steps with abnormal work efficiency Ta are at the local minimum point positions of the construction progress curve, it means that there is an abnormality in the construction progress of the building project. In order to eliminate the influence of environmental data on the decline of the work efficiency of construction workers and thus on the construction progress of the building project, analyze the environmental data with abnormal work efficiency, and count the number of environmental suitability in the environmental data with abnormal work efficiency that is lower than the preset environmental suitability threshold, specifically including:

[0073] For the abnormal time step Ta of work efficiency, count the number Nlow of time steps in this time step where the environmental suitability score S(ti) is lower than the preset environmental suitability threshold Sth. The formula is as follows:

[0074] where 1(S(ti) < Sth) means that at time step ti, it takes the value of 1 when S(ti) < Sth, and 0 otherwise;

[0075] If it indicates that this number is less than half of the number of time steps in the abnormal time step of work efficiency, then it means that the historical environmental data has no impact on the work efficiency of construction workers. Therefore, the decline in the construction progress may be caused by other factors;

[0076] Otherwise, it means that the historical environmental data has an impact on the work efficiency of construction workers, resulting in a decline in the construction progress of the building project. Then extract the historical environmental data with environmental suitability lower than the preset environmental suitability threshold from the abnormal environmental data of work efficiency and send it to the management staff for processing.

[0077] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. The construction engineering supervision quality assessment system based on big data technology is characterized by: The system comprises a data collection module (100), a work abnormality analysis module (200), an environmental abnormality analysis module (300) and a comprehensive analysis module (400), wherein: The data collection module (100) collects construction personnel work data, historical environmental data and construction progress data; The work abnormality analysis module (200) establishes a construction personnel work efficiency curve based on time steps according to the construction personnel work data, and determines a local minimum point in the curve as a work efficiency abnormality time step; The environmental anomaly analysis module (300) calculates the environmental suitability score of each time step in the historical environmental data; and sorts the historical environmental data in the order of the time steps based on the time step and the environmental suitability score as the result; and selects the historical environmental data and the environmental suitability score corresponding to the work efficiency abnormal time step as the work efficiency abnormal environmental data according to the sorting result; The comprehensive analysis module (400) establishes a construction progress curve based on time steps according to the construction progress data; and selects the corresponding time step position of the construction progress curve according to the abnormal work efficiency time step, and determines whether it is at the local minimum point position of the construction progress curve, wherein: If it is at the local minimum point of the construction progress curve, analyze the abnormal working efficiency environment data and count the number of abnormal working efficiency environment data whose environmental suitability is lower than the preset environmental suitability threshold, where: If this number is less than half of the number of time steps in the abnormal work efficiency time step, it means that the historical environmental data has no impact on the work efficiency of the construction personnel; Otherwise, it means that the historical environmental data has an impact on the work efficiency of the construction personnel, and the historical environmental data with an environmental suitability lower than the preset environmental suitability threshold in the abnormal work efficiency environmental data is extracted and sent to the management personnel for processing.

2. The construction engineering supervision quality assessment system based on big data technology according to claim 1 is characterized in that: The construction personnel work data in the data collection module (100) include working hours, completed tasks and task complexity; the historical environmental data include temperature, humidity, wind speed and rainfall factors.

3. The construction engineering supervision quality assessment system based on big data technology according to claim 2 is characterized in that: The process of the work abnormality analysis module (200) establishing a construction personnel work efficiency curve based on time steps and determining a local minimum point in the curve specifically includes: Define the calculation formula for the work efficiency of construction personnel, which is work efficiency = (amount of completed tasks * task complexity coefficient) / working hours; The time step refers to the time interval at which the system records construction data, and the end time of each day is used as a unified time step unit; The work efficiency of the corresponding construction personnel is calculated at each time step, and a construction personnel work efficiency curve based on the time step is drawn. The X-axis represents the time step, and the Y-axis represents the work efficiency of the construction personnel at the time step. The curve reflects the changing trend of the work efficiency of the construction personnel over time. By detecting the local minimum points in the work efficiency curve, the decline in the work efficiency of the construction workers is determined. If the work efficiency of the construction workers at a certain time step is lower than the work efficiency of the construction workers at a time step in the field, the time step is determined to be a local minimum point and is used as an abnormal work efficiency time step.

4. The construction engineering supervision quality assessment system based on big data technology according to claim 3 is characterized in that: The environmental anomaly analysis module (300) comprises an environmental suitability calculation unit (301), and the process of the environmental suitability calculation unit (301) calculating the environmental suitability score of each time step in the historical environmental data specifically comprises: Set the appropriate interval and corresponding weight of each environmental factor in the historical environmental data. The appropriate interval for temperature factor is set to 15℃-25℃; the appropriate interval for humidity factor is set to 30%-60%; the appropriate interval for wind speed factor is set to 0-10 m / s; the appropriate interval for rainfall factor is set to 0 mm; By comparing with the corresponding suitable interval, the suitability sub-score of each environmental factor is calculated. For the temperature suitability sub-score, according to the degree of deviation between the temperature and the corresponding suitable interval, a linear function is used to convert the temperature into a score. When the temperature is between 15℃-25℃, the score is 1, when the temperature is higher than 35℃ or lower than 5℃, the score is 0, and in other cases, the score gradually decreases according to the degree of deviation; for the humidity suitability sub-score, when the humidity is in the range of 30%-60%, the score is 1, when it is higher or lower than this range, the score gradually decreases, and finally when it is lower than 20% or higher than 80%, the score is 0; for the wind speed suitability sub-score, when the wind speed is 0-10 m / s, the score is 1, when it exceeds 10 m / s, the score decreases, and when the wind speed is greater than 20 m / s, the score is 0; for the rainfall suitability sub-score, when there is no rainfall, the score is 1, and when there is rainfall, the score is 0; Multiply each environmental factor suitability sub-score by its corresponding weight, and then add all weighted scores to obtain the environmental suitability score, where the weights are assigned according to the importance of different environmental factors to the impact of construction; According to the above calculation process of environmental suitability score, the environmental suitability score is calculated for the environmental factors corresponding to each time step in the historical environmental data to obtain a value between 0 and 1.

5. The construction engineering supervision quality assessment system based on big data technology according to claim 4 is characterized in that: The abnormal environment analysis module (300) comprises an abnormal environment analysis unit (302), and the specific process of the abnormal environment analysis unit (302) determining the abnormal working efficiency environment data comprises: Using the same time step in the work abnormality analysis module (200), the historical environmental data is sorted in the order of the time step, and the environmental suitability scores are added to the sorted historical environmental data according to the time step to generate a final sorted list; The work efficiency abnormal time step calculated in the work abnormality analysis module (200) is compared with the time step in the final sorting list, and the historical environmental data and environmental suitability score corresponding to the same time step are selected as the work efficiency abnormal environmental data.

6. The construction engineering supervision quality assessment system based on big data technology according to claim 5 is characterized in that: The process of the comprehensive analysis module (400) determining whether a time step in the abnormal work efficiency time step is at a local minimum point position of the construction progress curve is as follows: By recording the construction progress value at each time step t, a construction progress curve is drawn, where the X-axis represents the time step t and the Y-axis represents the construction progress P(t); The local minimum point of the construction progress curve is defined as follows: If P(t) satisfies P(t) < P(t - 1) and P(t) < P(t + 1), then the time step t is the local minimum point; Set the time steps of abnormal work efficiency as Ta, where Ta = {t1, t2,..., ta}. If P(ta) satisfies the definition of the local minimum point of the construction progress and the number of satisfied cases is greater than half of the number of time steps in the time steps of abnormal work efficiency, it means that the positions of the time steps in the time steps of abnormal work efficiency Ta are at the local minimum point positions of the construction progress curve.

7. The construction engineering supervision quality assessment system based on big data technology according to claim 6 is characterized in that: The specific process of the comprehensive analysis module (400) analyzing the environmental data of abnormal work efficiency is as follows: For the time steps of abnormal work efficiency Ta, count the number of time steps Nlow in which the environmental suitability score S(ti) is lower than the preset environmental suitability threshold Sth in this time step. The formula is as follows: where 1(S(ti)<Sth) means that at time step ti, it takes the value of 1 when S(ti)<Sth, and 0 otherwise; like If the number is less than half of the number of time steps in the abnormal work efficiency time step, it means that the historical environmental data has no impact on the work efficiency of the construction personnel; Otherwise, it means that the historical environmental data has an impact on the work efficiency of construction workers. Then, extract the historical environmental data with environmental suitability lower than the preset environmental suitability threshold in the environmental data of abnormal work efficiency and send it to the management staff for processing.

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