CN-121980624-A - Municipal engineering quality assessment method and system based on big data

CN121980624ACN 121980624 ACN121980624 ACN 121980624ACN-121980624-A

Abstract

The invention discloses a municipal engineering quality assessment method and system based on big data, and relates to the technical field of municipal engineering quality management. The method comprises the following steps of creating a unique identification code for municipal engineering projects, binding engineering data to form a data chain, comparing check data with standard data to generate a first difference signal, comparing actual cost with budget cost to generate a second difference signal, performing logic consistency check to generate a first risk signal, calculating a comprehensive risk value according to the first difference signal and the first risk signal, generating a first early warning signal when a third threshold value is exceeded, and triggering spot check. According to the invention, tamper-proof data is constructed by creating the unique identification code and the data chain, the fake making behavior affecting the engineering quality is identified through logic consistency check, and the accurate allocation of supervision resources according to risk values is realized by adopting a dynamic risk threshold matrix and a self-adaptive spot check algorithm, so that the initiative and the accuracy of quality supervision are comprehensively improved.

Inventors

DING YANXIA
YU JU
HAO JIAXING
Xin Yunda
CHEN SHIQI
Jia Zhenghuan
WANG YINGPING
CHANG FUZENG
LUO XIAOLING

Assignees

云南业信规划发展集团有限公司

Dates

Publication Date: 20260505
Application Date: 20260331

Claims (10)

1. The municipal engineering quality assessment method based on big data is characterized by comprising the following steps of: Step S1, creating a unique identification code for municipal engineering projects, and binding cost data, data and inspection data with the unique identification code to form a data chain; The data comprise preset standard data, acceptance conclusion data and flow data; The cost data comprises budget cost data, historical project cost data and actual cost data; S2, comparing the test data with preset standard data to generate a first difference signal, and comparing the actual cost data with the budget cost data to generate a second difference signal; step S21, according to the association relation between the unique identification code and the data chain, a first threshold corresponding to the preset standard data is matched for each item of to-be-evaluated inspection data, and a second threshold corresponding to the budget cost data and the historical project cost data is matched for each item of to-be-evaluated cost data; s22, comparing the test data with preset standard data, and calculating a deviation value; step S23, judging whether the first threshold value is exceeded according to the deviation value, comprising: if the deviation value does not exceed the first threshold value, checking the checking data to obtain a checking result, and binding and updating the checking result and the corresponding checking data into a data chain; If the deviation value exceeds a first threshold value, generating a first difference signal, wherein the first difference signal comprises a deviation item, a deviation value, an occurrence time, an occurrence position and responsibility unit information; The deviation item is a data field of the inspection data, the occurrence time is a time mark in the unique identification code, the occurrence position is an item code and a construction stage code in the unique identification code, and the responsibility unit information is flow data in a data chain; S24, comparing the budget cost data with the actual cost data, and calculating a unit cost deviation rate of the main subentry engineering; Step S25, judging whether the second threshold is exceeded according to the unit cost deviation rate, comprising: If the unit cost deviation rate does not exceed the second threshold, the cost data passes the verification, and the verification passing result and the corresponding cost data are bound and updated into a data chain; if the unit cost deviation rate exceeds a second threshold, generating a second difference signal, wherein the second difference signal comprises a cost anomaly item, the unit cost deviation rate and related amount information; Step S3, performing logic consistency check on a data chain for generating a first difference signal and a second difference signal to generate a first risk signal; And S4, calculating a comprehensive risk value according to the first difference signal and the first risk signal, and generating a first early warning signal and triggering spot check when the comprehensive risk value exceeds a third threshold value.
2. The big data based municipal works quality assessment method according to claim 1, wherein: the preset standard data comprise engineering design drawings, technical specifications, material report qualification certificates, construction schemes and geological investigation reports; The material verification qualification comprises hydration reaction activation energy parameters and material component composition ratio parameters; The acceptance conclusion data comprises a branch engineering acceptance record, a unit engineering quality completion acceptance record, a completion chart and a comprehensive verification evaluation report; The flow data comprises a material approach report and inspection list, a construction log, a material approach bill and an engineering money payment application record; The inspection data comprise environment and working condition data, field inspection records, image data and laboratory detection report data, wherein the environment and working condition data comprise stress parameters, displacement parameters, temperature parameters and humidity parameters; the actual cost data includes actual material procurement cost data, equipment rental cost data, and labor cost data.
3. The method for evaluating the quality of municipal works based on big data according to claim 2, wherein the step S1 specifically comprises: step S11, setting a unique identification code for municipal engineering projects, wherein the unique identification code comprises project codes, construction stage codes, time identifiers and verification identifiers; step S12, format standardization is carried out on the cost data, the data and the checking data, and corresponding unique identification codes are added; Step S13, calculating digital abstract values of the data, the check data and the cost data after format standardization; And S14, storing the data units added with the unique identification codes and the digital digest values in time sequence, and embedding the digest values of the preceding data units into the following data units to form a data chain.
4. The method for evaluating the quality of municipal works based on big data according to claim 3, wherein comparing the inspection data with the preset standard data, calculating the deviation value comprises the following steps: When the inspection data are laboratory detection report data and environment and working condition data, the corresponding preset standard data are technical specifications, engineering design drawings and geological investigation reports, the optical character recognition and natural language processing technology is utilized to analyze the reference parameters from the preset standard data, the acquisition numerical values in the inspection data are extracted, and the difference value between the reference parameters and the acquisition numerical values is calculated to obtain the deviation value; When the inspection data are image data, the corresponding preset standard data are engineering design drawings, the computer vision technology is utilized to extract the actual space coordinates in the image data, the engineering design drawings are input into BIM software to generate three-dimensional model coordinates for space comparison, and the space distance difference between the actual space coordinates and the three-dimensional model coordinates is calculated to obtain an offset value; When the inspection data is the on-site inspection record, the corresponding preset standard data is the construction scheme and the material report qualification certificate, the actual construction procedure, the used material model and the construction process characteristics in the on-site inspection record are extracted, natural language semantic comparison is carried out on the actual construction procedure, the used material model and the construction process characteristics with the standard procedure and the material properties specified in the construction scheme and the material report qualification certificate, and the inconsistent characteristic item number is used as a deviation value.
5. The big data based municipal works quality assessment method of claim 4, wherein the logical consistency check comprises a material consistency check, a space-time consistency check, a workload consistency check, a cost consistency check, and a physical consistency check; The material consistency verification comprises the steps of extracting material information in a laboratory detection report, comparing the material information with material information in a material approach report inspection list, verifying whether the total inventory consumption record is larger than or equal to the laboratory inspection amount, and judging that the materials information is not matched or the total inventory consumption record is smaller than the laboratory inspection amount, so that the materials are in logic contradiction; The space-time consistency check comprises the steps of extracting metadata information in image data, wherein the metadata information comprises GPS coordinates and a time stamp, comparing the GPS coordinates with the current construction position and the recording date recorded in a construction log, and judging that the GPS coordinates are not in the current construction position or the time stamp is inconsistent with the recording date, and judging that the GPS coordinates are in logic contradiction; the work load consistency check comprises the steps of comparing the actual engineering quantity which is identified and calculated based on the environment and working condition data with the quantity on a material inventory, and judging that the logic contradictions exist if the minimum material consumption quantity which is theoretically necessary for completing the actual engineering quantity is continuously and obviously higher than the quantity on the material inventory; The cost consistency check comprises the steps of searching the sub-project generating the second difference signal, searching the check data and the flow data corresponding to the sub-project, and judging that the logic contradiction exists if the actual cost data of the sub-project is lower than the second threshold value, but all indexes of the check data of the sub-project are higher than the standard qualified line and the flow data is complete and has no loss; The physical consistency check comprises the steps of extracting temperature parameters and humidity parameters in environment and working condition data, extracting hydration reaction activation energy parameters and material component distribution ratio parameters in material verification qualification according to unique identification codes, carrying out integral calculation on the temperature parameters and the humidity parameters along a time axis to obtain a physical property evolution limit value, carrying out inverse verification pseudo-comparison on an actual test index recorded in laboratory detection report data and the physical property evolution limit value, and judging that the physical evolution is normal logical contradiction if the actual test index is higher than the physical property evolution limit value plus a preset tolerance.
6. The big data based municipal works quality assessment method of claim 5, wherein the first risk signal comprises a first grade, a second grade, and a third grade: the first level indicates that there is a slight logical conflict; the second level indicates that there is an explicit logical conflict; The third level indicates that there is a serious logical conflict.
7. The big data based municipal works quality assessment method according to claim 6, wherein the calculation expression of the comprehensive risk value is: ; wherein R is a comprehensive risk value, S 1 is a first difference signal, F (S 1 ) is a frequency and severity function based on a first difference signal set, the frequency and severity function is determined by the number of the first difference signals, the amplitude exceeding a first threshold value, and the duration of the first difference signals, S 2 is a first risk signal, L (S 2 ) is a rank quantization value in which a first risk signal rank is mapped to a numerical value, C represents a credit rating of a construction unit, the credit rating is generated according to quality performance, contract performance, and datagram quality of a construction unit history item, a range of values is [0,1], β is a weight coefficient of the rank quantization value, γ is a weight coefficient of the construction unit credit rating, δ is a weight coefficient of the frequency and severity function, and β+γ+δ=1, μ is a physical rule penalty coefficient; The level quantized value comprises a first level mapped to a value L 1 , a second level mapped to a value L 2 and a third level mapped to a value L 3 , and the value is 0< L 1 <L 2 <L 3 and less than or equal to 1; when the physical consistency check determines that there is no logical contradiction, μ=1; When the physical consistency check determines that the physical evolution is a logical contradiction of normal physical evolution, μ=k, where k is a preset penalty amplification constant greater than 1.
8. The method for evaluating quality of municipal works based on big data according to claim 7, wherein generating the first warning signal when the integrated risk value exceeds the third threshold value comprises: Establishing a threshold query matrix, wherein the row dimension of the threshold query matrix is an engineering structure importance level, the column dimension is a construction stage risk coefficient, and the value of a third threshold is in a decreasing trend along the row direction and the column direction of the matrix; the engineering structure importance grade is divided into a first importance grade, a second importance grade and a third importance grade; the risk factors in the construction stage are divided into a low risk factor, a medium risk factor and a high risk factor; Dynamically acquiring and setting a corresponding third threshold value by querying the threshold value query matrix based on the importance level of the engineering structure of the current to-be-evaluated inspection data and the risk coefficient of the current construction stage; if the comprehensive risk value exceeds a third threshold value, generating a first early warning signal, wherein the first early warning signal comprises a risk item identifier, a current risk value, a risk level and suggested treatment measures; triggering the selective examination according to the first early warning signal, wherein the selective examination comprises random selective examination and directional selective examination, and specifically comprises the following steps: If the first risk signal is not generated or the first risk signal is of a first level and a second level, generating random spot check to obtain a spot check result, wherein the random spot check comprises a spot check object, a spot check position, the number of samples n and a spot check priority; if the first risk signal is of the third level, converting the first risk signal into a directional sampling test to obtain a sampling test result; If the sampling result exceeds a first threshold or a second threshold corresponding to the sampling object, generating a corresponding risk signal and binding the corresponding risk signal to a data chain, wherein the sampling result is used for carrying out feedback optimization on risk assessment parameters, and the risk assessment parameters comprise alpha, beta, gamma, the first threshold and the second threshold; Generating the corresponding risk signal includes: if the sampling inspection is at a position or a data item where no risk signal is generated, and the sampling inspection result exceeds a first threshold or a second threshold corresponding to the sampling inspection object, generating a new first difference signal or a new second difference signal, and binding the first difference signal or the second difference signal and the sampling inspection result to a data chain; If the spot check is at the position or the data item where the first difference signal or the second difference signal or the first risk signal is generated and the spot check result exceeds the first threshold or the second threshold corresponding to the spot check object, updating the state of the original risk signal to be verified.
9. The big data based municipal works quality assessment method of claim 8, wherein the calculation expression of the sample number n of random spot check is: ; Wherein n base is a basic sampling sample size preset according to the project scale, R is a comprehensive risk value, the comprehensive risk value is normalized to an interval [0,1], alpha is a risk amplification coefficient larger than 0 and is used for adjusting the sensitivity degree of risks to sampling intensity, As a round-up function.
10. Municipal works quality evaluation system based on big data, its characterized in that includes collection module, analysis module, check module and early warning module: the acquisition module is used for creating a unique identification code for municipal engineering projects, and binding cost data, data and inspection data with the unique identification code to form a data chain; The data comprise preset standard data, acceptance conclusion data and flow data; The cost data comprises budget cost data, historical project cost data and actual cost data; The analysis module is used for comparing the test data with preset standard data to generate a first difference signal, and comparing the actual cost data with the budget cost data to generate a second difference signal; step S21, according to the association relation between the unique identification code and the data chain, a first threshold corresponding to the preset standard data is matched for each item of to-be-evaluated inspection data, and a second threshold corresponding to the budget cost data and the historical project cost data is matched for each item of to-be-evaluated cost data; s22, comparing the test data with preset standard data, and calculating a deviation value; step S23, judging whether the first threshold value is exceeded according to the deviation value, comprising: if the deviation value does not exceed the first threshold value, checking the checking data to obtain a checking result, and binding and updating the checking result and the corresponding checking data into a data chain; If the deviation value exceeds a first threshold value, generating a first difference signal, wherein the first difference signal comprises a deviation item, a deviation value, an occurrence time, an occurrence position and responsibility unit information; The deviation item is a data field of the inspection data, the occurrence time is a time mark in the unique identification code, the occurrence position is an item code and a construction stage code in the unique identification code, and the responsibility unit information is flow data in a data chain; S24, comparing the budget cost data with the actual cost data, and calculating a unit cost deviation rate of the main subentry engineering; Step S25, judging whether the second threshold is exceeded according to the unit cost deviation rate, comprising: If the unit cost deviation rate does not exceed the second threshold, the cost data passes the verification, and the verification passing result and the corresponding cost data are bound and updated into a data chain; if the unit cost deviation rate exceeds a second threshold, generating a second difference signal, wherein the second difference signal comprises a cost anomaly item, the unit cost deviation rate and related amount information; The verification module is used for carrying out logic consistency verification on a data chain for generating a first difference signal and a second difference signal and generating a first risk signal; the early warning module is used for calculating a comprehensive risk value according to the first difference signal and the first risk signal, generating a first early warning signal and triggering spot check when the first early warning signal exceeds a third threshold value.

Description

Municipal engineering quality assessment method and system based on big data Technical Field The invention relates to the technical field of municipal engineering quality management, in particular to a municipal engineering quality assessment method and system based on big data. Background In recent years, municipal engineering quality supervision mainly depends on manual spot check and post-inspection, and has pain points such as data dispersion, supervision lag and the like. The traditional means is difficult to realize whole process data tracking, lacks the correlation analysis of cost and quality data, and cannot effectively find hidden problems such as material stealth and material reduction. Meanwhile, the existing method lacks an intelligent risk assessment model, and the supervision resource allocation is unreasonable. Along with the development of big data technology, a quality evaluation method capable of integrating multi-source data and realizing intelligent risk identification and dynamic early warning is needed to improve supervision efficiency and accuracy. At present, in the China invention with the publication number of CN120317759A, a road municipal engineering construction quality supervision system based on the Internet of things is disclosed, by comparing real-time collection of construction quality data with standard data, potential risks can be found in data fluctuation and abnormal changes can be tracked, fluctuation trend analysis is carried out by combining multiple parameters, so that various quality index changes can be monitored in real time, abnormal fluctuation can be identified and processed in time, the real-time performance and accuracy of construction quality supervision are improved, and meanwhile, the response of problems from finding to processing is quicker, and the construction efficiency and quality are effectively improved. However, the related technology does not build a tamper-proof data system by creating a unique identification code and an embedded data chain, is not beneficial to guaranteeing the authenticity and traceability of engineering data, cannot identify the fake making behavior affecting the municipal engineering quality through a logic checking mechanism, has certain limitation, cannot adopt a dynamic risk threshold matrix and a self-adaptive sampling inspection algorithm to realize accurate allocation of quality supervision resources according to risk values, and improves the pertinence and efficiency of quality supervision. Disclosure of Invention The technical problem solved by the invention is that the related technology does not build a tamper-proof data system by creating a unique identification code and an embedded data chain, is not beneficial to ensuring the authenticity and traceability of engineering data, cannot identify the fake making behavior affecting the municipal engineering quality through a logic verification mechanism, has a certain limitation, cannot adopt a dynamic risk threshold matrix and a self-adaptive sampling inspection algorithm to realize accurate allocation of quality supervision resources according to risk values, and improves the pertinence and efficiency of quality supervision. In order to solve the technical problems, the invention provides the following technical scheme: the municipal engineering quality assessment method based on big data comprises the following steps: Step S1, creating a unique identification code for municipal engineering projects, and binding cost data, data and inspection data with the unique identification code to form a data chain; The data comprise preset standard data, acceptance conclusion data and flow data; The cost data comprises budget cost data, historical project cost data and actual cost data; S2, comparing the test data with preset standard data to generate a first difference signal, and comparing the actual cost data with the budget cost data to generate a second difference signal; step S21, according to the association relation between the unique identification code and the data chain, a first threshold corresponding to the preset standard data is matched for each item of to-be-evaluated inspection data, and a second threshold corresponding to the budget cost data and the historical project cost data is matched for each item of to-be-evaluated cost data; s22, comparing the test data with preset standard data, and calculating a deviation value; step S23, judging whether the first threshold value is exceeded according to the deviation value, comprising: if the deviation value does not exceed the first threshold value, checking the checking data to obtain a checking result, and binding and updating the checking result and the corresponding checking data into a data chain; If the deviation value exceeds a first threshold value, generating a first difference signal, wherein the first difference signal comprises a deviation item, a deviation value, an occurrence time, an occurrence po