Highway asset value assessment method and device, electronic equipment and storage medium

Abstract

The application provides a highway asset value evaluation method and device, electronic equipment and a storage medium. The highway asset value evaluation method comprises the following steps: if a new maintenance event is detected, a target evaluation unit corresponding to the maintenance event is determined; an intervention efficiency coefficient of the target evaluation unit and current road condition data are obtained, and predicted road condition data after the target evaluation unit is subjected to the maintenance event is predicted based on the intervention efficiency coefficient and the current road condition data; cumulative value gain of the target evaluation unit is calculated based on the predicted road condition data and the current road condition data, and a first dynamic asset value model is constructed based on the cumulative value gain and the predicted road condition data; and asset value of the target evaluation unit is evaluated based on the first dynamic asset value model. The first dynamic asset value model takes the cumulative maintenance value gain of the target evaluation unit as a basic value, thereby calculating a real-time changing dynamic asset value, and realizing accurate evaluation of the asset value of the target evaluation unit.

Description

Technical Field

[0001] This invention relates to the field of highway asset management technology, and in particular to a method, apparatus, electronic device and storage medium for highway asset valuation. Background Technology

[0002] As a crucial infrastructure, the management of highway asset value is of great significance for fiscal budgeting, maintenance decisions, asset transfer, and performance evaluation. Traditional highway asset valuation methods are mainly divided into cost approach (historical cost, replacement cost), income approach (discounted future income), and market approach. Traditional valuation methods have significant limitations, including static nature, disconnect from actual road conditions, and disconnect from maintenance projects, failing to accurately reflect the dynamic value of highway assets. Furthermore, existing models fail to incorporate different types of maintenance inputs and their effects as value variables into the valuation system, resulting in a lack of asset value considerations in maintenance decisions and hindering the maximization of asset value throughout its entire lifecycle. While some research attempts to incorporate road conditions into asset valuation, most remain at the level of simple linear relationships or static mappings, failing to construct a dynamic model capable of responding to various maintenance events, updating in real time, and possessing machine learning adaptive capabilities. In addition, existing integrated highway maintenance management platforms have accumulated massive amounts of road condition detection data, historical maintenance data, and traffic volume data, but the value of this data has not been fully explored to serve dynamic asset value assessment. Summary of the Invention

[0003] This invention provides a method, apparatus, electronic device, and storage medium for assessing the value of highway assets, in order to overcome the deficiencies in the prior art.

[0004] This invention provides a method for valuing highway assets, comprising: If a new maintenance event is detected, the target assessment unit corresponding to the maintenance event is determined; wherein, the target assessment unit is obtained based on the basic assessment units that divide the highway by station number; Obtain the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit; Based on the predicted road condition data and the current road condition data, the cumulative value gain of the target evaluation unit is calculated, and based on the cumulative value gain and the predicted road condition data, a first dynamic asset value model is constructed. The asset value of the target assessment unit is assessed based on the first dynamic asset value model.

[0005] According to a highway asset valuation method provided by the present invention, the step of constructing a first dynamic asset valuation model based on the cumulative value gain and the predicted road condition data includes: Obtain the current base value of the target evaluation unit and calculate the remaining lifetime coefficient of the target evaluation unit; Based on the predicted road condition data, the value state coefficient of the target evaluation unit is calculated; The first dynamic asset value model is constructed based on the cumulative value gain, the current basic value, the remaining life coefficient, and the value state coefficient.

[0006] According to a highway asset valuation method provided by the present invention, after valuing the asset value of the target valuation unit based on the first dynamic asset valuation model, the method further includes: Monitor the actual road condition data after the maintenance event is implemented on the target assessment unit; Based on the actual road condition data and the predicted road condition data, the intervention effectiveness coefficient and value gain coefficient in the first dynamic asset value model are corrected.

[0007] According to a highway asset valuation method provided by the present invention, the step of correcting the intervention effectiveness coefficient and value gain coefficient in the first dynamic asset valuation model based on the actual road condition data and the predicted road condition data includes: Based on the actual road condition data and the predicted road condition data, calculate the road condition prediction error; Based on the road condition prediction error, the weighted recursive least squares method is used to correct the intervention effectiveness coefficient and the value gain coefficient.

[0008] According to a highway asset valuation method provided by the present invention, after valuing the asset value of the target valuation unit based on the first dynamic asset valuation model, the method further includes: Obtain the estimated cost of performing the maintenance event; Calculate the value gain based on the estimated cost, the current road condition data, and the predicted road condition data; The value enhancement efficiency is calculated based on the value gain and the estimated cost.

[0009] The highway asset valuation method provided by the present invention further includes: If no new maintenance event is detected, construct a second dynamic asset value model for each of the basic assessment units in which the maintenance event has not been implemented within the target time frame; Based on the second dynamic asset value model, the rate of value decay of the basic valuation unit within the target time range is calculated.

[0010] According to a highway asset valuation method provided by the present invention, after valuing the asset value of the target valuation unit based on the first dynamic asset valuation model, the method further includes: Based on the first dynamic asset value model, the changes in the asset value of the target evaluation unit are dynamically displayed in the form of a map.

[0011] The present invention also provides a highway asset valuation device, comprising: The determination module is configured to determine the target evaluation unit corresponding to the new maintenance event if a new maintenance event is detected; wherein the target evaluation unit is based on the basic evaluation units that divide the highway by station number; The prediction module is configured to acquire the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit. The first calculation module is configured to calculate the cumulative value gain of the target evaluation unit based on the predicted road condition data and the current road condition data, and to construct a first dynamic asset value model based on the cumulative value gain and the predicted road condition data. The evaluation module is configured to evaluate the asset value of the target evaluation unit based on the first dynamic asset value model.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the highway asset valuation methods described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the highway asset valuation method as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the highway asset valuation methods described above.

[0015] The present invention provides a method, apparatus, electronic device, and storage medium for assessing highway asset value. It pre-divides the highway into several basic assessment units according to rules. Then, upon detecting a new maintenance event, it determines the target assessment unit corresponding to the maintenance event, obtains the intervention effectiveness coefficient and current road condition data of the target assessment unit, and predicts the road condition data after implementing the maintenance event on the target assessment unit based on the intervention effectiveness coefficient and current road condition data. Based on the predicted road condition data and current road condition data, it calculates the cumulative value gain of the target assessment unit, and constructs a first dynamic asset value model based on the cumulative maintenance value gain of the target assessment unit. This first dynamic asset value model uses the cumulative maintenance value gain of the target assessment unit as its base value, thereby calculating the real-time changing dynamic asset value and achieving accurate assessment of the asset value of the target assessment unit. Through the technical solution provided by this invention, the transformation of highway asset value assessment from static to dynamic quantification is realized, and a scientific basis is provided for measuring the value return of maintenance projects. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the highway asset valuation method provided by the present invention.

[0018] Figure 2 This is a schematic diagram of the structure of the highway asset valuation device provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] Figure 1 This is a flowchart illustrating a highway asset valuation method according to an exemplary embodiment. Figure 1As shown in an exemplary embodiment, the highway asset valuation method includes steps 110 to 140, which are described in detail below.

[0022] Step 110: If a new maintenance event is detected, determine the target evaluation unit corresponding to the maintenance event; wherein the target evaluation unit is obtained based on the basic evaluation units that divide the highway by station number.

[0023] In this embodiment of the invention, the highway is divided into several basic assessment units according to its chainage. Following the principles of refined management, data accessibility, and asset homogeneity, a three-level division method is adopted for assessment unit division. Direction-lane-kilometer markers are used as the main units for road surface assets, and structure names are used as the units for bridge and tunnel assets. These units are then integrated and linked in the database through spatial location. Specific rules are as follows: Level 1: Classified by route and direction. Since the traffic load, road condition deterioration patterns and maintenance history of the up and down roads may differ significantly, it is more scientific to evaluate them as independent assets. Therefore, the traditional highway is set as the main management line and classified according to the direction of travel.

[0024] Level Two: Classification by asset type and lanes. Asset types include roadbed, pavement, and traffic safety facilities. When classifying by lanes, for example, a four-lane highway can be divided into: uphill lane - first lane (overtaking lane), uphill lane - second lane (driving lane), downhill lane - first lane, and downhill lane - second lane. Bridges, tunnels, and culverts are assessed as individual structures (i.e., road assets). Ancillary facilities (toll plazas, service areas) are divided by functional blocks, such as toll plaza pavement, service area main building, and parking lot.

[0025] Level 3: Divided by the smallest unit of maintenance work. Building upon Level 2, linear assets such as road surfaces can be further subdivided into fixed lengths (e.g., 1 kilometer or 100 meters). This aligns with the data collection intervals of automated road condition detection (e.g., inspection vehicles) and the work units for routine maintenance and minor repairs, facilitating the direct application of assessment results to the generation of maintenance work orders and performance evaluation.

[0026] Establish a structured numbering rule, following the sequence: [Route Code] - [Direction Code] - [Starting Chainage] - [Ending Chainage / Asset Type Code] - [Lane Code]. The route code is typically G102 or S301; the direction code is U (uphill / direction 1) or D (downhill / direction 2); and the chainage uses a standard chainage format, such as K100+000. For 100-meter road segments, this can be simplified to K100100, representing K100+100. For linear assets (pavement, roadbed), the ending chainage is entered, such as K101+000. For point / structure assets, the type code and sequence number are entered, such as BRG01 (Bridge 1), TUN02 (Tunnel 2), and SLP03 (Slope 3). Lane codes include L1 (first lane), L2 (second lane), and SHL (hard shoulder).

[0027] After processing according to the above numbering rules, the following example can be generated: Road surface unit: G102-U-K100+000-K101+000-L2 (second lane of G102 National Highway northbound section from K100 to K101); Bridge Unit: G102-U-K105+121-BRG01 (Bridge No. 1 at K105+121 on the northbound lane of National Highway G102); 100-meter section unit: S301-D-K100100-K100200-L1 (the first lane of the section from K100+100 to K100+200 on the southbound lane of Provincial Highway S301).

[0028] If a new maintenance event is detected, the target assessment unit corresponding to the maintenance event is determined, that is, it is determined which basic assessment unit the maintenance event is for.

[0029] Step 120: Obtain the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit.

[0030] In this embodiment of the invention, a highway asset evaluation index model library is pre-built, which includes: Basic Value Indicators (BVI): These are derived from traditional assessment methods such as replacement cost (C_replacement), design life (L_design), and used life (T_used).

[0031] Dynamic Depreciation Index (DDI): This index reflects the natural depreciation of an asset due to time and use. The core is the Road Condition Index (CSI), which can be further subdivided into the Pavement Condition Index (PCI) and Bridge Condition Index (BCI). A natural depreciation model of road conditions without maintenance intervention is established, such as the exponential depreciation model: CSI_natural(t) = CSI_0 × exp(-λ × t), where λ is the depreciation coefficient.

[0032] Value Intervention Indicator (VII): Quantifies the impact of various maintenance activities on asset value. Two key parameters are defined for each type of maintenance work (e.g., routine cleaning, crack repair, micro-surfacing, milling overlay, major repair): Intervention effectiveness coefficient (η): Represents the percentage of a maintenance project's ability to "repair" road conditions from their current state to an ideal state, indicating the project's repair efficiency (between 0 and 1). For example: η=1 indicates that the project can restore the asset to its original condition (such as large-scale milling and repaving).

[0033] η=0.1~0.3 indicates that the project can only slightly improve road conditions (such as sealing cracks).

[0034] η=0 indicates that the project has no direct impact on road conditions (such as routine cleaning).

[0035] Value gain function (ΔV(I)): represents the total increase in asset value resulting from the implementation of a maintenance project, and is a function of its investment cost (I) and intervention effectiveness.

[0036] When a new maintenance event is detected, the intervention effectiveness coefficient η and the current road condition data CSI_old of the target assessment unit are obtained, and the road condition data CSI_new after the maintenance event is implemented on the target assessment unit is predicted as CSI_new = CSI_old + η × (CSI_max - CSI_old).

[0037] Step 130: Based on the predicted road condition data and the current road condition data, calculate the cumulative value gain of the target evaluation unit, and construct a first dynamic asset value model based on the cumulative value gain and the predicted road condition data.

[0038] In this embodiment of the invention, based on predicted road condition data and current road condition data, the cumulative value gain of the target evaluation unit is calculated, that is, the value gain of the current maintenance event on the target evaluation unit is calculated using the following formula: ΔV(I)=α×I+β×[F(CSI_new)-F(CSI_old)]×BVI(t).

[0039] Where α and β are weighting coefficients, also known as value gain coefficients; I represents the investment cost of this maintenance event; A represents the capital conversion factor, 0 < α < 1, indicating what proportion of the investment can be directly converted into asset value, with an initial value of 0.7; β represents the value premium coefficient, β > 0, indicating the value amplification effect brought about by the improvement of road conditions, with an initial value of 1.0; [F(CSI_new) - F(CSI_old)] × BVI(t) represents the basic value released due to the improvement of road conditions.

[0040] The calculated value gain includes not only the capitalization of the investment cost I of the maintenance event, but also the value corresponding to the increase in the F(CSI) coefficient due to the improvement in road conditions.

[0041] The cumulative value gain of the target evaluation unit is calculated using the following formula: ΣΔV(I)=ΣΔV(I)_previous+ΔV(I); Wherein, ΣΔV(I)_previous represents the cumulative value gain of the target evaluation unit before the maintenance event was performed.

[0042] Based on the calculated cumulative value gain and predicted road condition data, a first dynamic asset value model is constructed.

[0043] Step 140: Evaluate the asset value of the target evaluation unit based on the first dynamic asset value model.

[0044] In this embodiment of the invention, the asset value of the target assessment unit is accurately assessed based on the first dynamic asset value model.

[0045] In this embodiment of the invention, the highway is pre-divided into several basic assessment units according to rules. Then, after a new maintenance event is detected, the target assessment unit corresponding to the maintenance event is determined. The intervention effectiveness coefficient and current road condition data of the target assessment unit are obtained. Based on the intervention effectiveness coefficient and current road condition data, the predicted road condition data after implementing the maintenance event on the target assessment unit is predicted. Based on the predicted road condition data and current road condition data, the cumulative value gain of the target assessment unit is calculated. Based on the cumulative value gain and predicted road condition data, a first dynamic asset value model is constructed. The first dynamic asset value model uses the cumulative maintenance value gain of the target assessment unit as the base value, thereby calculating the real-time changing dynamic asset value and achieving accurate assessment of the asset value of the target assessment unit. Through the technical solution provided by this invention, the transformation of highway asset value from static assessment to dynamic quantification is realized, and a scientific basis for measuring the value return of maintenance projects is provided.

[0046] In an exemplary embodiment of the present invention, constructing a first dynamic asset value model based on the cumulative value gain and the predicted road condition data includes: Obtain the current base value of the target evaluation unit and calculate the remaining lifetime coefficient of the target evaluation unit; Based on the predicted road condition data, the value state coefficient of the target evaluation unit is calculated; The first dynamic asset value model is constructed based on the cumulative value gain, the current basic value, the remaining life coefficient, and the value state coefficient.

[0047] In this embodiment of the invention, the current basic value of the target assessment unit is read from the highway asset evaluation index model library: BVI(t) = C_replacement × (1 - T_used / L_design). The value status coefficient F (CSI_current) and remaining useful life coefficient G (T_remaining(t)) of the target assessment unit are then calculated.

[0048] The first dynamic asset value model (DAV) constructed is shown below: DAV(t)=[BVI(t)+ΣΔV(I)]×F(CSI(t))×G(T_remaining(t)); Where: DAV(t) represents the dynamic asset value of the target assessment unit at time t. BVI(t) represents the basic value of the target assessment unit at time t, usually expressed as net replacement cost, BVI(t) = C_replacement × (1 - T_used / L_design). ΣΔV(I) represents the cumulative present value of all maintenance works value increments over the asset lifecycle of the target assessment unit.

[0049] F(CSI(t)) represents the value state coefficient, which is the core function that establishes a quantitative relationship between road condition and value. It is an S-shaped curve (Sigmoid function) or a piecewise function with a range of (0,1). Its calculation formula is shown below: F(CSI)=F_min+(F_max-F_min) / (1+exp(-k×(CSI-CSI_mid))); Where: CSI represents the input road condition data, such as PCI, ranging from 0 to 100. F_min represents the lower limit of the function, usually set to 0.1, indicating that when the road condition is extremely poor (PCI=0), the asset value is only 10% of its base value (almost worthless). F_max represents the upper limit of the function, usually set to 1.0, indicating that when the road condition is perfect (PCI=100), the asset value fully reflects its base value. CSI_mid represents the road condition data corresponding to the inflection point, usually set to 75 (the lower limit of good road condition), which is the critical point where value changes rapidly. k represents the steepness of the curve (sensitivity coefficient), usually set to 0.1. The larger the k value, the steeper the curve, meaning that slight changes in road conditions will cause large fluctuations in value.

[0050] When road conditions are excellent, F approaches 1; when road conditions are poor, F approaches 0. Example: F(CSI) = 1 / (1 + exp(-k × (CSI - CSI_threshold))), where k is the sensitivity coefficient and CSI_threshold is the baseline road condition.

[0051] G(T_remaining(t)) represents the remaining life coefficient, T_remaining = L_design - T_used. The G function ensures that when the remaining life approaches 0, the asset value also approaches 0.

[0052] In an exemplary embodiment of the present invention, after assessing the asset value of the target assessment unit based on the first dynamic asset value model, the method further includes: Monitor the actual road condition data after the maintenance event is implemented on the target assessment unit; Based on the actual road condition data and the predicted road condition data, the intervention effectiveness coefficient and value gain coefficient in the first dynamic asset value model are corrected.

[0053] In this embodiment of the invention, the actual road condition data after the maintenance event is implemented on the target assessment unit is continuously monitored. The actual road condition data is compared with the predicted intersection data CSI_new. The intervention effectiveness coefficient (η) and value gain coefficient (α,β) are corrected in reverse through machine learning algorithms (such as gradient descent), so that the first dynamic asset value model becomes more and more accurate.

[0054] In an exemplary embodiment of the present invention, the step of correcting the intervention effectiveness coefficient and value gain coefficient in the first dynamic asset value model based on the actual road condition data and the predicted road condition data includes: Based on the actual road condition data and the predicted road condition data, calculate the road condition prediction error; Based on the road condition prediction error, the weighted recursive least squares method is used to correct the intervention effectiveness coefficient and the value gain coefficient.

[0055] In this embodiment of the invention, after a maintenance event is completed and a predetermined effect observation period (e.g., 6 months or 1 year) has elapsed, a learning process is automatically triggered. The following data is extracted from the database and matched according to the target evaluation unit and the maintenance event type: the road condition data CSI_old (i.e., the aforementioned current road condition data) of the target evaluation unit before maintenance, the type and investment cost I of this maintenance event, the actual road condition data CSI_actual detected at the end of the effect observation period after the implementation of the maintenance event, the historical traffic volume data of the target evaluation unit, and the predicted road condition data CSI_new of the aforementioned predicted maintenance event.

[0056] Calculate the road condition prediction error: Error = CSI_actual - CSI_new.

[0057] The coefficients are updated using the weighted recursive least squares method. The advantage of the weighted recursive least squares method is that it assigns higher weights to new data, enabling the model to adapt to changes quickly.

[0058] Specifically, the gain vector and covariance matrix of this maintenance event are calculated. Based on the road condition prediction error, the correction amount Δη of the intervention effectiveness coefficient η is calculated. The intervention effectiveness coefficient is then updated: η_new = η_old + Δη. Boundary constraints are applied to η_new to ensure it falls within a reasonable range (e.g., 0.05 ≤ η ≤ 0.95).

[0059] The learned η_new is updated in the highway asset evaluation index model library, replacing the old η_old.

[0060] Record the process of each correction to form a learning log. When the same type of project accumulates to a certain number (e.g., 10 times) in different units, a statistical significance test can be performed on η, and the value of η can be further subdivided according to traffic load level.

[0061] In this embodiment of the invention, the DAV of all basic assessment units can be automatically aggregated to obtain the total asset value of road segments, routes, and even the entire road network company. Total asset value = Σ(DAV_i of all basic assessment units i).

[0062] In an exemplary embodiment of the present invention, after assessing the asset value of the target assessment unit based on the first dynamic asset value model, the method further includes: Obtain the estimated cost of performing the maintenance event; Calculate the value gain based on the estimated cost, the current road condition data, and the predicted road condition data; The value enhancement efficiency is calculated based on the value gain and the estimated cost.

[0063] In this embodiment of the invention, the efficiency of value enhancement brought about by each dollar invested after implementing a maintenance event on a certain target assessment unit is calculated. This is a key indicator for measuring the efficiency of maintenance fund utilization. The calculation formula is as follows: VPE_ij = ΔV_ij / I_ij= α_j + [β_j × (F(CSI_new) - F(CSI_old)) ×BVI(t)] / I_ij; Where: VPE_ij represents the value enhancement efficiency (meta-value / meta-cost) of implementing maintenance event j on target assessment unit i. ΔV_ij represents the value gain brought to target assessment unit i by implementing maintenance event j. F(CSI_new)-F(CSI_old) represents the change in value state coefficient ΔF. I_ij represents the estimated cost required for implementing maintenance event j on target assessment unit i.

[0064] In one exemplary embodiment of the present invention, the highway asset valuation method further includes: If no new maintenance event is detected, construct a second dynamic asset value model for each of the basic assessment units in which the maintenance event has not been implemented within the target time frame; Based on the second dynamic asset value model, the rate of value decay of the basic valuation unit within the target time range is calculated.

[0065] In this embodiment of the invention, if no new maintenance event is detected, the value decay rate of each basic assessment unit within the target time range is calculated. The value decay rate refers to the amount of loss in the dynamic value (DAV) of a highway asset per unit time without maintenance intervention. It comprehensively reflects the rate of value loss caused by natural aging and traffic load.

[0066] The rate of value decay is calculated using the following formula: VDR_i =[DAV(t)-DAV(t+Δt)] / Δt; Where VDR_i represents the value decay rate of basic assessment unit i (unit: yuan / year). DAV(t) represents the dynamic asset value of the basic assessment unit at the current time t. DAV(t+Δt) represents the predicted dynamic asset value at a future time t+Δt (i.e., the target time range), assuming no maintenance. Δt represents the prediction period, typically 1 year.

[0067] Obtain the current state of each basic assessment unit i: DAV(t), CSI(t). Use the natural decay model to predict the traffic data after time Δt: CSI(t+Δt)=f(CSI(t),λ, traffic volume). For example, CSI(t+Δt)=CSI(t)×exp(-λ×Δt).

[0068] Calculate the base value of each basic evaluation unit after time Δt: BVI(t+Δt)=C_replacement×(1-(T_used+Δt) / L_design).

[0069] Assuming there are no maintenance events, the cumulative value gain remains unchanged: ΣΔV(I)(t+Δt)=ΣΔV(I)_t.

[0070] Construct the second dynamic asset value model: DAV(t+Δt)=[BVI(t+Δt)+ΣΔV(I)_t]×F(CSI(t+Δt))×G(T_remaining-Δt).

[0071] Calculate the rate of value decay: VDR_i=(DAV(t)-DAV(t+Δt)) / Δt.

[0072] The larger the basic valuation unit VDR_i, the more rapidly its asset value is being lost, making it a priority target.

[0073] In an exemplary embodiment of the present invention, after assessing the asset value of the target assessment unit based on the first dynamic asset value model, the method further includes: Based on the first dynamic asset value model, the changes in the asset value of the target evaluation unit are dynamically displayed in the form of a map.

[0074] In this embodiment of the invention, the latest road condition monitoring data, traffic volume data, and maintenance project plans and completion data are obtained in real time or near real time through a data interface with the highway integrated maintenance management platform.

[0075] Based on the aforementioned technical solution, asset valuation is automatically triggered, and the spatiotemporal changes in the asset value of each basic valuation unit are dynamically displayed on the platform's GIS map in the form of heat maps, trend maps, etc. Each basic valuation unit will generate a dynamic report, which includes: current DAV, F(CSI) rating (Excellent, Good, Average, Poor), historical value curve, maintenance records, and value gain.

[0076] In this embodiment of the invention, the technical solution provided by the present invention is described by the following two examples: Example 1: Taking a 1-kilometer section of a highway as an example, its replacement cost C_replacement is 20 million yuan, L_design is 15 years, and T_used is 5 years. Initial state: Current road condition data PCI_old is 85. Maintenance event: Planned investment of 800,000 yuan for micro-surfacing preventative maintenance. From the highway asset evaluation index model library, the η for micro-surfacing is found to be 0.4.

[0077] Calculate the basic value: BVI = 2000 × (1 - 5 / 15) = 13,333,300 yuan; Calculate the predicted road condition data: PCI_new = 85 + 0.4 × (100 - 85) = 91; Calculate the value gain: Assume the original F(PCI_old) = 0.88 and the new F(PCI_new) = 0.95. ΔV(I) = 0.7 × 80 + 0.3 × (0.95 - 0.88) × 1333.33 ≈ 56 + 28 = 840,000 yuan. Here, α = 0.7 and β = 0.3, meaning that the value gain is mainly contributed by direct capital investment and road condition improvement, and the weights are adjustable. Calculate the dynamic asset value: DAV_new=[1333.33+84]×0.95×G(10)≈13.47 million yuan. G(10) represents the coefficient of the remaining 10 years of life, assumed to be 0.98.

[0078] It is evident that the 800,000 yuan maintenance investment not only curbed the natural decline in asset value but also brought an additional 280,000 yuan in value gain by improving road conditions, increasing the total asset value from approximately 11.73 million yuan (13.3333 million yuan × 0.88) before maintenance to 13.47 million yuan. This quantitative result fully demonstrates the value of the maintenance investment.

[0079] Example 2: Taking the highway unit GX-U-K50+000-K51+000-L1 as an example. The initial state is C_replacement=3.5 million yuan, L_design=15 years, T_used=6 years, and the current PCI_old=80.

[0080] Calculate the basic value: BVI = 350 × (1 - 6 / 15) = 2.1 million yuan.

[0081] Calculate F(80): Let F_min=0.1, F_max=1.0, CSI_mid=75, k=0.1, then F(80)=0.1+0.9 / (1+exp(-0.1×(80-75)))≈0.77.

[0082] DAV_initial=210×0.77×(9 / 15)≈970,000 yuan.

[0083] For the maintenance event of milling and overlaying the basic assessment unit, the investment is I = 1 million yuan, the intervention effectiveness coefficient is η = 0.85, the value gain coefficient is α = 0.75, and β = 1.0.

[0084] Value intervention calculation: PCI_predicted = 80 + 0.85 × (100 - 80) = 97; F(97)=0.1+0.9 / (1+exp(-0.1×(97-75)))≈0.98; ΔV(I) = 0.75 × 100 + 1.0 × (0.98 - 0.77) × 210 = 75 + 44.1 = 1,191,000 yuan.

[0085] Post-maintenance value assessment: DAV_new=[210+119.1]×0.98×(9 / 15)≈329.1×0.98×0.6≈1.935 million yuan.

[0086] Therefore, the maintenance investment of RMB 1 million increased the asset value of the basic assessment unit from RMB 970,000 to RMB 1,935,000, a net increase of RMB 965,000, which significantly realized the preservation and appreciation of the asset.

[0087] One year later, actual detection data (PCI_actual=95) of the basic assessment unit was collected. The road condition prediction error was then calculated as Error=95-97=-2. The learning algorithm was automatically activated, and the intervention effectiveness coefficient η was fine-tuned from 0.85 to 0.83, making the model more realistic.

[0088] In this embodiment of the invention, the value of a highway asset is defined as the present value of the service capacity it can provide based on its continuously changing service state (road condition) over its remaining service life. The concept of Dynamic Asset Value (DAV) is proposed, transforming asset value from a static snapshot into a continuous time-series variable. By constructing a complex evaluation index model library integrating basic value, cumulative maintenance gain, value state coefficient, and remaining life coefficient, various maintenance events are treated as value intervention events. A quantitative relationship is established between maintenance investment, road condition improvement, and asset value gain. A value intervention quantification mechanism and a machine learning-based model self-correction mechanism are designed, enabling the model to learn and evolve from historical data and achieving real-time dynamic updates of value using big data technology. Simultaneously, asset valuation is deeply embedded into the daily maintenance management workflow, realizing the integration and intelligentization of asset management and maintenance operations.

[0089] The following describes the highway asset valuation device provided by the present invention. The highway asset valuation device described below can be referred to in correspondence with the highway asset valuation method described above. It should be noted that the device provided in the following embodiments and the method provided in the above embodiments belong to the same concept, and the specific way in which each module and unit performs its operation has been described in detail in the method embodiments, and will not be repeated here.

[0090] In one exemplary embodiment of the present invention, please refer to Figure 2 , Figure 2 This is an exemplary embodiment of a highway asset valuation device, comprising the following modules.

[0091] The determination module 210 is configured to determine the target evaluation unit corresponding to the maintenance event if a new maintenance event is detected; wherein the target evaluation unit is based on the basic evaluation units that divide the highway by station number; The prediction module 220 is configured to acquire the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit. The first calculation module 230 is configured to calculate the cumulative value gain of the target evaluation unit based on the predicted road condition data and the current road condition data, and to construct a first dynamic asset value model based on the cumulative value gain and the predicted road condition data. The evaluation module 240 is configured to evaluate the asset value of the target evaluation unit based on the first dynamic asset value model.

[0092] In an exemplary embodiment of the present invention, the first computing module 230 includes: The first calculation submodule is configured to obtain the current base value of the target evaluation unit and calculate the remaining lifetime coefficient of the target evaluation unit. The second calculation submodule is configured to calculate the value state coefficient of the target evaluation unit based on the predicted road condition data. A submodule is configured to construct the first dynamic asset value model based on the cumulative value gain, the current basic value, the remaining life coefficient, and the value state coefficient.

[0093] In an exemplary embodiment of the present invention, the highway asset valuation device further includes: The monitoring module is configured to monitor actual road condition data after the maintenance event is performed on the target assessment unit; The correction module is configured to correct the intervention effectiveness coefficient and value gain coefficient in the first dynamic asset value model based on the actual road condition data and the predicted road condition data.

[0094] In one exemplary embodiment of the present invention, the correction module includes: The third calculation submodule is configured to calculate the road condition prediction error based on the actual road condition data and the predicted road condition data. The correction submodule is configured to correct the intervention effectiveness coefficient and the value gain coefficient using a weighted recursive least squares method based on the road condition prediction error.

[0095] In an exemplary embodiment of the present invention, the highway asset valuation device further includes: The acquisition module is configured to acquire the estimated cost of implementing the maintenance event; The second calculation module is configured to calculate the value gain based on the estimated cost, the current road condition data, and the predicted road condition data. The third calculation module is configured to calculate the value improvement efficiency based on the value gain and the estimated cost.

[0096] In an exemplary embodiment of the present invention, the highway asset valuation device further includes: The module is configured to build a second dynamic asset value model for each of the basic assessment units in the target time range if no new maintenance event is detected; The fourth calculation module is configured to calculate the value decay rate of the basic assessment unit within the target time range based on the second dynamic asset value model.

[0097] In an exemplary embodiment of the present invention, the highway asset valuation device further includes: The display module is configured to dynamically display the changes in the asset value of the target evaluation unit in the form of a map, based on the first dynamic asset value model.

[0098] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340, wherein the processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a highway asset valuation method, which includes: if a new maintenance event is detected, determining the target valuation unit corresponding to the maintenance event; wherein the target valuation unit is based on basic valuation units that divide the highway by station number. Obtain the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit; Based on the predicted road condition data and the current road condition data, the cumulative value gain of the target evaluation unit is calculated, and based on the cumulative value gain and the predicted road condition data, a first dynamic asset value model is constructed. The asset value of the target assessment unit is assessed based on the first dynamic asset value model.

[0099] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute the highway asset valuation method provided by the above methods, the method including: if a new maintenance event is detected, determining the target valuation unit corresponding to the maintenance event; wherein, the target valuation unit is based on the basic valuation units that divide the highway by chainage; Obtain the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit; Based on the predicted road condition data and the current road condition data, the cumulative value gain of the target evaluation unit is calculated, and based on the cumulative value gain and the predicted road condition data, a first dynamic asset value model is constructed. The asset value of the target assessment unit is assessed based on the first dynamic asset value model.

[0101] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the highway asset valuation method provided by the above methods, the method comprising: if a new maintenance event is detected, determining a target valuation unit corresponding to the maintenance event; wherein the target valuation unit is based on a basic valuation unit that divides the highway by chainage; Obtain the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit; Based on the predicted road condition data and the current road condition data, the cumulative value gain of the target evaluation unit is calculated, and based on the cumulative value gain and the predicted road condition data, a first dynamic asset value model is constructed. The asset value of the target assessment unit is assessed based on the first dynamic asset value model.

[0102] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for valuing highway assets, characterized in that, include: If a new maintenance event is detected, the target assessment unit corresponding to the maintenance event is determined; wherein, the target assessment unit is obtained based on the basic assessment units that divide the highway by station number; Obtain the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit; Based on the predicted road condition data and the current road condition data, the cumulative value gain of the target evaluation unit is calculated, and based on the cumulative value gain and the predicted road condition data, a first dynamic asset value model is constructed. The asset value of the target assessment unit is assessed based on the first dynamic asset value model.

2. The highway asset valuation method according to claim 1, characterized in that, The construction of the first dynamic asset value model based on the cumulative value gain and the predicted road condition data includes: Obtain the current base value of the target evaluation unit and calculate the remaining lifetime coefficient of the target evaluation unit; Based on the predicted road condition data, the value state coefficient of the target evaluation unit is calculated; The first dynamic asset value model is constructed based on the cumulative value gain, the current basic value, the remaining life coefficient, and the value state coefficient.

3. The highway asset valuation method according to claim 1, characterized in that, After assessing the asset value of the target assessment unit based on the first dynamic asset valuation model, the method further includes: Monitor the actual road condition data after the maintenance event is implemented on the target assessment unit; Based on the actual road condition data and the predicted road condition data, the intervention effectiveness coefficient and value gain coefficient in the first dynamic asset value model are corrected.

4. The highway asset valuation method according to claim 3, characterized in that, The step of correcting the intervention effectiveness coefficient and value gain coefficient in the first dynamic asset value model based on the actual road condition data and the predicted road condition data includes: Based on the actual road condition data and the predicted road condition data, calculate the road condition prediction error; Based on the road condition prediction error, the weighted recursive least squares method is used to correct the intervention effectiveness coefficient and the value gain coefficient.

5. The highway asset valuation method according to claim 1, characterized in that, After assessing the asset value of the target assessment unit based on the first dynamic asset valuation model, the method further includes: Obtain the estimated cost of performing the maintenance event; Calculate the value gain based on the estimated cost, the current road condition data, and the predicted road condition data; The value enhancement efficiency is calculated based on the value gain and the estimated cost.

6. The method for valuing highway assets according to claim 1, characterized in that, Also includes: If no new maintenance event is detected, construct a second dynamic asset value model for each of the basic assessment units in which the maintenance event has not been implemented within the target time frame; Based on the second dynamic asset value model, the rate of value decay of the basic valuation unit within the target time range is calculated.

7. The highway asset valuation method according to claim 1, characterized in that, After assessing the asset value of the target assessment unit based on the first dynamic asset valuation model, the method further includes: Based on the first dynamic asset value model, the changes in the asset value of the target evaluation unit are dynamically displayed in the form of a map.

8. A highway asset valuation device, characterized in that, include: The determination module is configured to determine the target evaluation unit corresponding to the new maintenance event if a new maintenance event is detected; wherein the target evaluation unit is based on the basic evaluation units that divide the highway by station number; The prediction module is configured to acquire the intervention effectiveness coefficient and current road condition data of the target evaluation unit, and based on the intervention effectiveness coefficient and the current road condition data, predict the predicted road condition data after the maintenance event is implemented on the target evaluation unit. The first calculation module is configured to calculate the cumulative value gain of the target evaluation unit based on the predicted road condition data and the current road condition data, and to construct a first dynamic asset value model based on the cumulative value gain and the predicted road condition data. The evaluation module is configured to evaluate the asset value of the target evaluation unit based on the first dynamic asset value model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the highway asset valuation method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the highway asset valuation method as described in any one of claims 1 to 7.

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