Road digital maintenance value and benefit quantitative evaluation model construction method

Abstract

The invention discloses a road digital maintenance value and benefit quantitative evaluation model construction method. The method comprises the following steps: digitization of a road maintenance period is divided into road maintenance equipment, road maintenance decision, road asset management and road patrol digitization; in road maintenance equipment, average detection time, detection precision, data connectivity, maintenance labor cost, operation safety level and equipment cost are selected as digital indexes; in the road maintenance decision, the maintenance decision efficiency and the maintenance cost are selected as digital indexes; in road asset management, asset management efficiency, management manpower cost, business management efficiency, asset data value rate and asset management cost are selected as digital indexes; in road patrol, patrol efficiency, patrol labor cost and event processing efficiency are selected as digital indexes; and establishing a quantitative model of the digital indexes. Based on this, it can be ensured that digital related elements of the road maintenance period can be quantified and calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of digitalization of highway infrastructure, and in particular to a method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance. Background Art

[0002] As the scale of highways continues to expand, the cost of maintenance and operation has also increased year by year. In recent years, with the deep integration of new generation information technologies such as artificial intelligence, big data, 5G, and data links with the field of highway transportation, the pace of digital transformation and upgrading of highways has been accelerated.

[0003] Highway maintenance is the basic guarantee for daily operation. How to quantitatively evaluate the value and benefits of highway digitalization is related to the economic operation cost and operational safety and reliability, and is an urgent problem to be solved in the development of the industry. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance.

[0005] The present invention discloses a method for constructing a highway digital maintenance value benefit quantitative evaluation model, comprising:

[0006] The digitization of highway maintenance period is divided into digitization of highway maintenance equipment, digitization of highway maintenance decision, digitization of highway asset management and digitization of highway inspection;

[0007] In the digitization of highway maintenance equipment, average detection time, detection accuracy, data connectivity, maintenance manpower cost, operation safety level and equipment cost are selected as digitization indicators, and a quantitative model of each digitization indicator is established to realize the calculation and evaluation of the digitization of each indicator;

[0008] In the digitalization of highway maintenance decision-making, maintenance decision-making efficiency and maintenance cost are selected as digital indicators, and a quantitative model of each digital indicator is established to realize the digital calculation and evaluation of each indicator;

[0009] In the digitalization of highway asset management, asset management effectiveness, management labor cost, business management efficiency, asset data value rate and asset management cost are selected as digitalization indicators, and a quantitative model for each digitalization indicator is established to realize the calculation and evaluation of the digitalization of each indicator;

[0010] In the highway inspection digitization, inspection efficiency, inspection manpower cost and event handling efficiency are selected as digitization indicators, and a quantitative model of each digitization indicator is established to realize the calculation and evaluation of the digitization of each indicator.

[0011] As a further improvement of the present invention,

[0012] The quantitative model of average detection time ADT (unit: hour) is:

[0013]

[0014] Where: SDT i is the single detection time of the ith time (unit: hour), N is the number of detections, and DPT is the data processing time (unit: hour);

[0015] The quantitative model of detection accuracy DA (unit: %) is:

[0016] DA=(1-DER)×DCR+EA (2)

[0017] Where: DER is the detection error rate (unit: %), DCR is the data coverage (unit: %), EA is the equipment accuracy (unit: %);

[0018] The quantitative model of data connectivity DC (unit: hour) is:

[0019]

[0020] Where: ADTT is the average time for data synchronization and transmission (unit: hours), DCMP is the data compatibility (unit: %);

[0021] The quantitative model of maintenance manpower cost MLC (unit: person-hour) is:

[0022] MLC=NIE×IT+NME×MT (4)

[0023] Where: NIE is the number of inspectors (unit: person), IT is the inspection time (unit: hour), NME is the number of maintenance workers (unit: person), MT is the maintenance operation time (unit: hour);

[0024] The quantitative model of the operational safety level OSL is:

[0025]

[0026] Where: OIR is the operation accident rate (unit: %), OFR is the operation mortality rate (unit: %), NMP is the number of field maintenance workers (unit: person);

[0027] The quantitative model of equipment cost EC (unit: yuan / year) is:

[0028]

[0029] Where: TEC is the total equipment cost (unit: yuan), AEST is the average equipment service life (unit: year).

[0030] As a further improvement of the present invention,

[0031] The quantitative model of maintenance decision efficiency MDE (unit: hour) is:

[0032]

[0033] Where: ADPT is the average time required for the decision-making process (unit: hours), DcRR is the decision rework rate (unit: %), and DISR is the decision success execution rate (unit: %);

[0034] The quantitative model of maintenance cost MC (unit: yuan) is:

[0035]

[0036] Where: TLC is the transportation and labor cost (unit: yuan), MMC is the maintenance material cost (unit: yuan), and MUE is the material utilization efficiency (unit: %).

[0037] As a further improvement of the present invention,

[0038] The quantitative model of asset management efficiency AME (unit: day) is:

[0039]

[0040] Where: AMCC is the asset management cycle (unit: day), AMUA is the accuracy of asset maintenance and update (unit: %), AUR is the asset utilization rate (unit: %), and AFR is the asset failure rate (unit: %);

[0041] The quantitative model of management manpower cost MHC (unit: person-day) is:

[0042] MHC×NAMP×MWT (10)

[0043] Where: NAMP is the number of asset management personnel (unit: person), MWT is the working time of management personnel (unit: day);

[0044] The quantitative model of business management efficiency OME (unit: hour) is:

[0045]

[0046] Where: BPT j is the business processing time (unit: hour), K is the number of business processes (unit: piece), BER j is the business error rate (unit: %);

[0047] The quantitative model of asset data value rate ADV (unit: %) is:

[0048] ADV=DTA×DUR×DSCR (12)

[0049] Where: DTA is data accuracy (unit: %), DUR is data utilization rate (unit: %), DSCR is decision support contribution rate (unit: %);

[0050] The quantitative model of asset management cost AMC (unit: yuan) is:

[0051] AMC×OPC+MRC+OAC (13)

[0052] In the formula: OPC is operating cost (unit: yuan), MRC is maintenance and repair cost (unit: yuan), OAC is other additional cost (unit: yuan).

[0053] As a further improvement of the present invention,

[0054] The quantitative model of inspection efficiency IE (unit: km / day) is:

[0055]

[0056] Where: AIT is the average inspection time (unit: day), IM is the inspection mileage (unit: kilometer);

[0057] The quantitative model of inspection manpower cost IHC (unit: person-day) is:

[0058] IHC=NIP×IWT (15)

[0059] Where: NIP is the number of asset management personnel (unit: person), IWT is the working time of management personnel (unit: day);

[0060] The quantitative model of event handling efficiency IRE (unit: day) is:

[0061]

[0062] Where: ART is the average incident response time (unit: day), AST is the average incident resolution time (unit: day), IRSR is the incident handling success rate (unit: %).

[0063] As a further improvement of the present invention, it also includes:

[0064] The quantitative models of various indicators in the digitization of highway maintenance equipment, the digitization of highway maintenance decision-making, the digitization of highway asset management and the digitization of highway inspection are stored on computing devices or storage devices respectively; the quantitative models corresponding to different stages of highway maintenance are selected and retrieved, and the corresponding indicator calculation results are obtained after the corresponding data are input, and the calculated indicator data are stored and output.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The present invention divides the highway maintenance period into four stages according to the actual highway maintenance content, namely, maintenance equipment, maintenance decision-making, asset management and inspection. In each stage, multiple indicators for quantifying the value and benefits of digital maintenance are selected, and a calculation method for each indicator is established, ultimately constructing a digital quantitative evaluation model for the highway maintenance period. This ensures that the digital-related elements of the highway maintenance period are quantifiable and calculable, providing support for cost saving and optimization of the maintenance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 The present invention discloses a flowchart of a method for constructing a quantitative evaluation model for the value and benefits of digital highway maintenance. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0069] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0070] like Figure 1 As shown, the present invention provides a method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance, comprising:

[0071] Step 1: Divide the digitization of highway maintenance period into digitization of highway maintenance equipment, digitization of highway maintenance decision, digitization of highway asset management and digitization of highway inspection;

[0072] Step 2: In the digitization of highway maintenance equipment, average detection time, detection accuracy, data connectivity, maintenance labor cost, operation safety level and equipment cost are selected as digitization indicators, and a quantitative model for each digitization indicator is established to realize the calculation and evaluation of the digitization of each indicator;

[0073] Specific:

[0074] Six first-level indicators and 16 second-level indicators were constructed, as shown in Table 1 below.

[0075] Table 1

[0076]

[0077]

[0078] Establish a quantitative model for each indicator to achieve digital calculation and evaluation;

[0079] The quantitative model of average detection time ADT (unit: hour) is:

[0080]

[0081] Where: SDT i is the single detection time of the ith time (unit: hour), N is the number of detections, and DPT is the data processing time (unit: hour);

[0082] The quantitative model of detection accuracy DA (unit: %) is:

[0083] DA=(1-DER)×DCR×EA (2)

[0084] Where: DER is the detection error rate (unit: %), DCR is the data coverage (unit: %), EA is the equipment accuracy (unit: %);

[0085] The quantitative model of data connectivity DC (unit: hour) is:

[0086]

[0087] Where: ADTT is the average time for data synchronization and transmission (unit: hours), DCMP is the data compatibility (unit: %);

[0088] The quantitative model of maintenance manpower cost MLC (unit: person-hour) is:

[0089] MLC=NIE×IT+NME×MT (4)

[0090] Where: NIE is the number of inspectors (unit: person), IT is the inspection time (unit: hour), NME is the number of maintenance workers (unit: person), MT is the maintenance work time (unit: hour);

[0091] The quantitative model of the operational safety level OSL is:

[0092]

[0093] Where: OIR is the operation accident rate (unit: %), OFR is the operation mortality rate (unit: %), NMP is the number of field maintenance workers (unit: person);

[0094] The quantitative model of equipment cost EC (unit: yuan / year) is:

[0095]

[0096] Where: TEC is the total equipment cost (unit: yuan), AEST is the average equipment service life (unit: year).

[0097] Step 3: In the digitalization of highway maintenance decision-making, maintenance decision-making efficiency and maintenance cost are selected as digital indicators, and a quantitative model for each digital indicator is established to realize the digital calculation and evaluation of each indicator;

[0098] Specific:

[0099] Two first-level indicators and six second-level indicators were constructed, as shown in Table 2 below.

[0100] Table 2

[0101]

[0102] Establish a quantitative model for each indicator to achieve digital calculation and evaluation;

[0103] The quantitative model of maintenance decision efficiency MDE (unit: hour) is:

[0104]

[0105] Where: ADPT is the average time required for the decision-making process (unit: hours), DcRR is the decision rework rate (unit: %), and DISR is the decision success execution rate (unit: %);

[0106] The quantitative model of maintenance cost MC (unit: yuan) is:

[0107]

[0108] Where: TLC is the transportation and labor cost (unit: yuan), MMC is the maintenance material cost (unit: yuan), and MUE is the material utilization efficiency (unit: %).

[0109] Step 4: In the digitalization of highway asset management, select asset management effectiveness, management labor cost, business management efficiency, asset data value rate and asset management cost as digitalization indicators, and establish a quantitative model for each digitalization indicator to achieve digital calculation and evaluation of each indicator;

[0110] Specific:

[0111] Five first-level indicators and 15 second-level indicators were constructed, as shown in Table 3 below.

[0112] Table 3

[0113]

[0114] Establish a quantitative model for each indicator to achieve digital calculation and evaluation;

[0115] The quantitative model of asset management efficiency AME (unit: day) is:

[0116]

[0117] Where: AMCC is the asset management cycle (unit: day), AMUA is the accuracy of asset maintenance and update (unit: %), AUR is the asset utilization rate (unit: %), and AFR is the asset failure rate (unit: %);

[0118] The quantitative model of management manpower cost MHC (unit: person-day) is:

[0119] MHC=NAMP×MWT (10)

[0120] Where: NAMP is the number of asset management personnel (unit: person), MWT is the working time of management personnel (unit: day);

[0121] The quantitative model of business management efficiency OME (unit: hour) is:

[0122]

[0123] Where: BPT j is the business processing time (unit: hour), K is the number of business processes (unit: piece), BER j is the business error rate (unit: %);

[0124] The quantitative model of asset data value rate ADV (unit: %) is:

[0125] ADV=DTA×DUR×DSCR (12)

[0126] Where: DTA is data accuracy (unit: %), DUR is data utilization rate (unit: %), DSCR is decision support contribution rate (unit: %);

[0127] The quantitative model of asset management cost AMC (unit: yuan) is:

[0128] AMC=OPC+MRC+OAC (13)

[0129] In the formula: OPC is operating cost (unit: yuan), MRC is maintenance and repair cost (unit: yuan), OAC is other additional cost (unit: yuan).

[0130] Step 5: In the digitalization of highway inspections, inspection efficiency, inspection labor cost, and event handling efficiency are selected as digital indicators, and a quantitative model for each digital indicator is established to achieve digital calculation and evaluation of each indicator;

[0131] Specific:

[0132] Three first-level indicators and seven second-level indicators are constructed, as shown in Table 4 below.

[0133] Table 4

[0134]

[0135] Establish a quantitative model for each indicator to achieve digital calculation and evaluation;

[0136] The quantitative model of inspection efficiency IE (unit: km / day) is:

[0137]

[0138] Where: AIT is the average inspection time (unit: day), IM is the inspection mileage (unit: kilometer);

[0139] The quantitative model of inspection manpower cost IHC (unit: person-day) is:

[0140] IHC=NIP×IWT (15)

[0141] Where: NIP is the number of asset management personnel (unit: person), IWT is the working time of management personnel (unit: day);

[0142] The quantitative model of event handling efficiency IRE (unit: day) is:

[0143]

[0144] Where: ART is the average incident response time (unit: day), AST is the average incident resolution time (unit: day), IRSR is the incident handling success rate (unit: %).

[0145] Based on the quantitative models of various indicators constructed, it also includes:

[0146] The quantitative models of various indicators in the digitization of highway maintenance equipment, the digitization of highway maintenance decision-making, the digitization of highway asset management and the digitization of highway inspection are stored separately on a computing device or a storage device (such as a computer), and the storage method can be classified storage; for example, after entering the application, four modules of the digitization of highway maintenance equipment, the digitization of highway maintenance decision-making, the digitization of highway asset management and the digitization of highway inspection are displayed. After clicking to enter the digitization module of highway maintenance equipment, the average detection time, detection accuracy, data connectivity, maintenance manpower cost, operation safety level and equipment cost indicator calculation module are displayed. After clicking to enter the detection accuracy module, the input boxes of the detection error rate, data coverage rate and equipment accuracy are displayed. The form of other indicator calculation modules is the same as that of the detection accuracy indicator calculation module; the quantitative models of various indicators in the digitization of highway maintenance decision-making, the digitization of highway asset management and the digitization of highway inspection are consistent with those of the digitization of highway maintenance equipment; at different stages of highway maintenance, the quantitative models corresponding to different stages are selected to be retrieved, and the corresponding indicator calculation results are obtained after inputting (manual input, drop-down box selection input or automatic input through relevant acquisition equipment) the corresponding data, and the calculated indicator data are stored and output.

[0147] Based on this, the calculation status of different indicators can be viewed through the computer, and the unfinished indicators can be quantified.

[0148] like:

[0149] Select highway maintenance equipment digitization on the corresponding application on the computer, and then the calculation module of average detection time, detection accuracy, data connectivity, maintenance labor cost, operation safety level and equipment cost will be displayed. Enter the detection accuracy calculation module, and the user can input the detection error rate, data coverage, and equipment accuracy to obtain the calculation result of the indicator-detection accuracy, and store, display and output it.

[0150] The advantages of the present invention are:

[0151] The present invention divides the highway maintenance period into four stages according to the actual highway maintenance content, namely, maintenance equipment, maintenance decision-making, asset management and inspection. In each stage, multiple indicators for quantifying the value and benefits of digital maintenance are selected, and a calculation method for each indicator is established, ultimately constructing a digital quantitative evaluation model for the highway maintenance period. This ensures that the digital-related elements of the highway maintenance period are quantifiable and calculable, providing support for cost saving and optimization of the maintenance process.

[0152] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance, characterized in that: include: The digitization of highway maintenance period is divided into digitization of highway maintenance equipment, digitization of highway maintenance decision, digitization of highway asset management and digitization of highway inspection; In the digitization of highway maintenance equipment, average detection time, detection accuracy, data connectivity, maintenance manpower cost, operation safety level and equipment cost are selected as digitization indicators, and a quantitative model for each digitization indicator is established; In the highway maintenance decision digitization, maintenance decision efficiency and maintenance cost are selected as digitization indicators, and a quantitative model of each digitization indicator is established; In the digitalization of highway asset management, asset management effectiveness, management labor cost, business management efficiency, asset data value rate and asset management cost are selected as digitalization indicators, and a quantitative model for each digitalization indicator is established; In the highway inspection digitization, inspection efficiency, inspection manpower cost and event handling efficiency are selected as digitization indicators, and a quantitative model for each digitization indicator is established.

2. The method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance as claimed in claim 1, characterized in that: The quantitative model of average detection time ADT (unit: hour) is: Where: SDT i is the single detection time of the ith time (unit: hour), N is the number of detections, and DPT is the data processing time (unit: hour); The quantitative model of detection accuracy DA (unit: %) is: DA=(1-DER)×DCR×EA (2) Where: DER is the detection error rate (unit: %), DCR is the data coverage (unit: %), EA is the equipment accuracy (unit: %); The quantitative model of data connectivity DC (unit: hour) is: Where: ADTT is the average time for data synchronization and transmission (unit: hours), DCMP is the data compatibility (unit: %); The quantitative model of maintenance manpower cost MLC (unit: person-hour) is: MLC=NIE×IT+NME×MT (4) Where: NIE is the number of inspectors (unit: person), IT is the inspection time (unit: hour), NME is the number of maintenance workers (unit: person), MT is the maintenance operation time (unit: hour); The quantitative model of the operational safety level OSL is: Where: OIR is the operation accident rate (unit: %), OFR is the operation mortality rate (unit: %), NMP is the number of field maintenance workers (unit: person); The quantitative model of equipment cost EC (unit: yuan / year) is: Where: TEC is the total equipment cost (unit: yuan), AEST is the average equipment service life (unit: year).

3. The method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance as claimed in claim 1, characterized in that: The quantitative model of maintenance decision efficiency MDE (unit: hour) is: Where: ADPT is the average time required for the decision-making process (unit: hours), DcRR is the decision rework rate (unit: %), and DISR is the decision success execution rate (unit: %); The quantitative model of maintenance cost MC (unit: yuan) is: Where: TLC is the transportation and labor cost (unit: yuan), MMC is the maintenance material cost (unit: yuan), and MUE is the material utilization efficiency (unit: %).

4. The method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance as claimed in claim 1, characterized in that: The quantitative model of asset management efficiency AME (unit: day) is: Where: AMCC is the asset management cycle (unit: day), AMUA is the accuracy of asset maintenance and update (unit: %), AUR is the asset utilization rate (unit: %), and AFR is the asset failure rate (unit: %); The quantitative model of management manpower cost MHC (unit: person-day) is: MHC=NAMP×MWT (10) Where: NAMP is the number of asset management personnel (unit: person), MWT is the working time of management personnel (unit: day); The quantitative model of business management efficiency OME (unit: hour) is: Where: BPT j is the business processing time (unit: hour), K is the number of business processes (unit: piece), BER j is the business error rate (unit: %); The quantitative model of asset data value rate ADV (unit: %) is: ADV = DTA × DUR × DSCR (12) Where: DTA is data accuracy (unit: %), DUR is data utilization (unit: %), and DSCR is decision support contribution rate (unit: %); The quantitative model of asset management cost AMC (unit: yuan) is: AMC=OPC+MRC+OAC (13) In the formula: OPC is operating cost (unit: yuan), MRC is maintenance and repair cost (unit: yuan), OAC is other additional cost (unit: yuan).

5. The method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance as claimed in claim 1, characterized in that: The quantitative model of inspection efficiency IE (unit: km / day) is: Where: AIT is the average inspection time (unit: day), IM is the inspection mileage (unit: kilometer); The quantitative model of inspection manpower cost IHC (unit: person-day) is: IHC=NIP×IWT (15) Where: NIP is the number of asset management personnel (unit: person), IWT is the working time of management personnel (unit: day); The quantitative model of event handling efficiency IRE (unit: day) is: Where: ART is the average incident response time (unit: day), AST is the average incident resolution time (unit: day), IRSR is the incident handling success rate (unit: %).

6. The method for constructing a quantitative evaluation model for the value and benefit of digital highway maintenance as claimed in claim 1, characterized in that: Also includes: The quantitative models of various indicators in the digitization of highway maintenance equipment, the digitization of highway maintenance decision-making, the digitization of highway asset management and the digitization of highway inspection are stored on computing devices or storage devices respectively; the quantitative models corresponding to different stages of highway maintenance are selected and retrieved, and the corresponding indicator calculation results are obtained after the corresponding data are input, and the calculated indicator data are stored and output.

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