Engineering machinery internet-of-things intelligent management system

Abstract

The invention provides an engineering machinery internet of things intelligent management system, and relates to the technical field of engineering machinery management, and the system comprises a sensing collection module which is used for collecting equipment state parameters and environment parameters of engineering machinery in real time; the spatio-temporal data processing module is used for processing, storing and analyzing equipment state parameters and environment parameters; the intelligent decision-making module is used for generating an intermediate decision-making instruction; the decision execution module is used for issuing the intermediate decision instruction to the target engineering machinery control system; and the ecological cooperation module is used for performing data interaction with an external ecological supervision system and executing an ecological compensation strategy. According to the invention, through the intelligent decision module based on the multi-target genetic algorithm, a Pareto optimal solution is realized among conflicting targets such as energy consumption, efficiency and fault rate; through an ecological red line dynamic matching algorithm, high-precision identification of a work forbidden zone boundary is realized, and by cooperating with migrant bird migration period data, the interference rate of a key ecological region is reduced, and the protection effect of biodiversity is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction machinery management, and particularly relates to a construction machinery Internet of Things intelligent management system. BACKGROUND

[0002] Construction machinery refers to heavy machinery equipment used in earthwork, building construction, mining, material handling and other fields, mainly including excavators, loaders, cranes, bulldozers, road rollers and the like. Its technical development relies on the comprehensive application of mechanical engineering, hydraulic transmission, electrical control, material science and intelligent technology. In terms of mechanical structure, construction machinery adopts high-strength steel and welding technology to ensure the stability and durability of the equipment under heavy load working conditions. The power system is usually driven by a diesel engine or an electric motor, combined with a transmission and a transmission device, to realize efficient energy conversion and power output. Hydraulic technology is one of the cores of construction machinery, which realizes precise force and motion control through hydraulic pumps, valves, cylinders and pipeline systems to meet the needs of different working scenarios. Modern construction machinery gradually integrates electronic control technology, such as electro-hydraulic proportional control, CAN bus communication and sensor monitoring, to improve the operation accuracy and automation level. In addition, remote monitoring, fault diagnosis and unmanned operation technology are becoming the development trend of the industry, which optimizes equipment management and operation efficiency with the help of Internet of Things (IoT) and artificial intelligence (AI). The progress of material science and manufacturing technology further improves the reliability of construction machinery, such as the use of wear-resistant coatings, lightweight design and modular structure, to adapt to complex working conditions and prolong the service life. In the future, with the in-depth application of new energy power, intelligent control and automation technology, construction machinery will continue to develop towards high efficiency, environmental protection and intelligence.

[0003] Generally, the construction machinery management system adopts a single time series or spatial analysis model, which is difficult to effectively capture the spatio-temporal coupling characteristics of the equipment state and environmental parameters, resulting in generally low accuracy of abnormal working condition recognition and long response delay. Moreover, the ecological protection measures often use fixed fences or post-event monitoring mode, lack of intelligent cooperation with real-time construction data, have high misentry rate in ecological sensitive areas, and the building material supply plan is disconnected with environmental protection requirements, causing resource waste.

[0004] In order to solve the defects in the prior art, the technical scheme provides a construction machinery Internet of Things intelligent management system. SUMMARY

[0005] The present application provides a construction machinery Internet of Things intelligent management system to solve the defects in the prior art.

[0006] In one aspect, the present application provides a construction machinery Internet of Things intelligent management system, comprising: A perception collection module is configured to collect equipment state parameters and environmental parameters of the engineering machinery in real time. A space-time data processing module is configured to process, store and analyze the equipment state parameters and the environmental parameters, and output standard parameter data. An intelligent decision module is configured to generate intermediate decision instructions based on the standard parameter data. A decision execution module is configured to issue the intermediate decision instructions to a target engineering machinery control system. An ecological coordination module is configured to interact with an external ecological supervision system, execute an ecological compensation strategy, update the intermediate decision instructions, and output final decision instructions.

[0007] According to the engineering machinery Internet of Things intelligent management system provided by the application, the space-time data processing module comprises a data processing unit, a storage unit and an analysis unit; the data processing unit is configured to preprocess the equipment state parameters and the environmental parameters to obtain preprocessed fusion parameter data; the storage unit is configured to store the preprocessed fusion parameter data to obtain fusion parameter storage data; and the analysis unit is configured to perform space-time analysis on the fusion parameter storage data to obtain standard parameter data.

[0008] According to the engineering machinery Internet of Things intelligent management system provided by the application, the step of performing space-time analysis by the analysis unit comprises: Retrieving the fusion parameter storage data to obtain key parameter data; Performing time series analysis and spatial analysis on the key parameter data to output time dimension analysis results and spatial dimension analysis results; Associating and fusing the time dimension analysis results and the spatial dimension analysis results to generate multi-dimensional analysis results; Normalizing and aggregating the multi-dimensional analysis results to output standard parameter data.

[0009] According to the engineering machinery Internet of Things intelligent management system provided by the application, the intelligent decision module comprises a rule engine unit, an optimization unit and a decision generation unit; the rule engine unit is configured to evaluate the standard parameter data based on predefined business rules to generate preliminary decision instructions; the optimization unit is configured to perform multi-objective optimization on the preliminary decision instructions to generate high-level decision instructions; and the decision generation unit is configured to confirm, resolve conflicts and encapsulate the high-level decision instructions to output intermediate decision instructions.

[0010] According to the engineering machinery Internet of Things intelligent management system provided by the application, the step of generating preliminary decision instructions by the rule engine unit comprises: Loading a trigger condition and action mapping table of a predefined business rule library; Matching the standard parameter data with the trigger condition of the rule based on the business rule library; According to the trigger condition, an initial action instruction set is generated; According to the initial action instruction set, a preliminary decision instruction is generated.

[0011] According to the engineering machinery Internet of Things intelligent management system provided by the application, the steps of multi-objective optimization of the optimization unit include: The preliminary decision instruction is parsed into a decision variable vector, and a multi-objective function is constructed based on the decision variable vector; Based on the multi-objective function, the initial instruction set is optimized by a multi-objective genetic algorithm, and solutions with a sensitivity higher than a threshold are removed to obtain an optimized instruction set; The decision variables of the optimized instruction set are encoded, and a high-level decision instruction is output.

[0012] According to the engineering machinery Internet of Things intelligent management system provided by the application, the steps of obtaining the optimized instruction set by the optimization unit through the multi-objective genetic algorithm include: The optimization parameters of the multi-objective genetic algorithm are initialized, and the decision variable vector is taken as input to construct an initial solution set; The multi-objective genetic algorithm is executed to iteratively optimize the initial solution set, and a Pareto front candidate solution set is generated; The sensitivity of the Pareto front candidate solution set is analyzed, the sensitivity index of each solution is calculated, and a sensitivity score is generated; The sensitivity score is compared with a preset sensitivity threshold, solutions with a sensitivity score higher than the preset sensitivity threshold are removed, and an optimized instruction set is obtained.

[0013] According to the engineering machinery Internet of Things intelligent management system provided by the application, the decision execution module includes an instruction formatting unit and an instruction issuing unit; the instruction formatting unit is used to convert the intermediate decision instruction into a compatible format of the engineering machinery control system, and output the formatted instruction; the instruction issuing unit is used to issue the formatted instruction to the target engineering machinery control system, and process the response feedback and monitor the execution state.

[0014] According to the engineering machinery Internet of Things intelligent management system provided by the application, the ecological coordination module includes a data interaction unit, an ecological compensation unit and a decision correction unit; the data interaction unit is used to interact with the external ecological supervision system, and send the state data of the engineering machinery, and receive the external ecological data; the ecological compensation unit is used to combine the state data and the external ecological data to generate an ecological compensation instruction; the decision correction unit is used to modify the intermediate decision instruction according to the ecological compensation instruction to obtain the final decision instruction.

[0015] According to the engineering machinery Internet of Things intelligent management system provided by the application, the steps of modifying the intermediate decision instruction by the strategy updating unit include: Based on the constraint condition in the ecological compensation instruction, the control parameter in the middle-level decision instruction is dynamically modified. When the external ecological data exceeds the preset threshold, the forced policy replacement mechanism is activated to generate a replacement decision instruction. The priority mapping rule of the ecological compensation instruction and the middle-level decision instruction is established, and the final decision instruction is output according to the rule conflict resolution result.

[0016] The engineering machinery Internet of Things intelligent management system provided by the application improves the spatio-temporal correlation analysis accuracy of the equipment state and the environmental parameters through the ARIMA-GIS hybrid analysis of the spatio-temporal data processing module. Through the intelligent decision module based on the NSGA-III multi-objective genetic algorithm, the Pareto optimal solution is realized among the conflicting targets such as energy consumption, efficiency and failure rate. Through the establishment of the priority mapping rule of the ecological compensation instruction and the middle-level decision instruction, a closed loop of "perception-analysis-decision-execution-feedback" is formed, so that the system can not only optimize the current decision with future data, but also dynamically balance the relationship between construction and ecology. Based on the GIS ecological red line dynamic matching algorithm, high-precision identification of the operation prohibited area boundary is realized, and the interference rate of the key ecological area is reduced by cooperating with the migratory cycle data of the migratory birds, so that the protection effect of the biological diversity is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0018] Figure 1 It is a kind of engineering machinery Internet of Things intelligent management system structure schematic diagram provided by the embodiment of the application; Figure 2 It is a kind of engineering machinery Internet of Things intelligent management system of the optimization unit of the embodiment of the application obtains the step diagram of preferred instruction set; Figure 3 It is a kind of engineering machinery Internet of Things intelligent management system of the strategy updating unit of the embodiment of the application carries out the step diagram of modification to middle-level decision instruction. DETAILED DESCRIPTION

[0019] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0020] Embodiment one: The present application is described below Figures 1-3 An engineering machinery Internet of Things intelligent management system.

[0021] As Figure 1 shown, the engineering machinery Internet of Things intelligent management system provided by the embodiment of the present application comprises a perception and collection module, a space-time data processing module, an intelligent decision module, a decision execution module and an ecological coordination module.

[0022] The perception and collection module is used for collecting the equipment state parameters and environmental parameters of the engineering machinery in real time. The equipment state parameters comprise engine speed, hydraulic system pressure, fuel level, working device position coordinates, driving speed and fault codes, etc. The environmental parameters comprise working area temperature and humidity, dust concentration, noise decibel, air quality index and geographic fence information, etc.

[0023] The space-time data processing module is used for processing, storing and analyzing the equipment state parameters and environmental parameters, and outputting standard parameter data. The space-time data processing module comprises a data processing unit, a storage unit and an analysis unit. The data processing unit is used for pre-processing the equipment state parameters and environmental parameters to obtain pre-processed fusion parameter data. The pre-processing steps comprise data cleaning (such as eliminating sensor drift data), format conversion, abnormal value detection and multi-source data space-time alignment (synchronization through time stamp and calibration through GPS coordinates). For abnormal value detection, the standard method is Z-score detection, and the formula is as follows:

[0024] Wherein, x is the original parameter value (for example, engine speed or temperature), μ is the sample mean, and σ is the sample standard deviation. The specific process is as follows: assuming that the parameter data point x obeys normal distribution, the sample mean μ and the sample standard deviation σ are calculated. The z measurement data point deviates from the mean value, and when ∣z∣>3 (i.e. exceeding 3 times the standard deviation), it is regarded as an abnormal value and eliminated.

[0025] Let the reference position coordinates be s ref , and the original collection coordinates s raw contain errors. After time stamp synchronization, the time deviation compensation term ϵ tThe corrected coordinates can ensure the multi-source data to be aligned in space-time. The formula of time stamp synchronization and GPS coordinate calibration is expressed as:

[0026]

[0027]

[0028] where s is a spatial coordinate vector, s ref is a reference position coordinate (ground truth point), s raw is an original collection coordinate, e t is a time synchronization deviation compensation (time-dependent term), t gps is a GPS time stamp, t sensor is a sensor collection time stamp, and k is a correction factor.

[0029] The storage unit is configured to store the preprocessed fusion parameter data to obtain fusion parameter storage data. The storage unit adopts a hybrid architecture of a distributed database cluster and a time series database, supports TB-level data parallel writing, and millisecond-level historical data retrieval.

[0030] The analysis unit is configured to perform space-time analysis on the fusion parameter storage data to obtain standard parameter data.

[0031] The steps of the space-time analysis performed by the analysis unit include: The analysis unit is configured to perform space-time analysis on the fusion parameter storage data to obtain standard parameter data.

[0032] The analysis unit is configured to perform space-time analysis on the fusion parameter storage data to obtain standard parameter data.

[0033] The formula for identifying the periodic fluctuation characteristics of the parameters using the ARIMA (p, d, q) model is expressed as:

[0034] where, ϕ(B) = 1 - ϕ1B -... - ϕ p B p is an autoregressive operator, θ(B) = 1 + θ1B +... + θ q B q is a moving average operator, B is a backshift operator, and d is the order of differencing. denotes the value of the time series data at time point τ, τ denotes the time index, ε τ denotes white noise error term, (mean 0, noise variance σ 2 ).

[0035] The specific process includes: First, perform stationarization, that is, perform d-order difference on the original sequence to obtain a stationary sequence, denoted as:

[0036] where d is determined according to ADF test, for example, if there is a trend, take d = 1. ADF test (Augmented Dickey-Fuller Test) is a statistical method for testing the stationarity of a time series, which is a key step in determining the order of difference (d) in ARIMA modeling.

[0037] Secondly, on the stationary sequence, fit an ARMA(p, q) model, denoted as:

[0038] where, is the sequence after difference.

[0039] Finally, solve the coefficients ϕ i and θ j by maximum likelihood estimation, ϕ i denotes the autoregressive coefficient (order i ∈ [1, p]). θ j denotes the moving average coefficient (order j ∈ [1, q]). And analyze the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the residual, detect periodicity (for example, the fluctuation period of engine speed). Autocorrelation function (ACF) and partial autocorrelation function (PACF) are the core tools for analyzing the structure of time series and determining the parameters (p and q) of ARIMA model. ACF is used to measure the linear correlation of time series with its own lag. PACF is used to measure the pure direct correlation of time series with its own lag after controlling intermediate lag terms.

[0040] For spatial analysis (GIS spatial interpolation), the inverse distance weight (IDW) method is used to generate heat maps, and the formula is expressed as:

[0041]

[0042] The unknown value of the target position s0 Make an estimate based on n nearby known points s b The value z(s b ), weight w b and the distance d(s0,s b ) to the power of p. The distance can be the Euclidean distance, expressed as:

[0043] Among them, s is the spatial coordinate, s0 is the target position point, s b Represents a known position point, index b∈[1,n], z(s b ): Position s b , d is the Euclidean distance, and w b is the weight factor, and α is the power parameter used to control the spatial influence range, usually α=2.

[0044] GIS (Geographic Information System) is a technical system used to collect, store, analyze, manage, and display geographic spatial data. Spatial interpolation is a core analytical method in GIS. It is used to predict the values ​​of unknown areas based on the attribute values ​​of known discrete points (such as temperature, precipitation, pollutant concentration, etc.) and generate continuous surfaces (such as rasters or contour lines). Inverse Distance Weighting (IDW) is one of the most commonly used deterministic spatial interpolation methods in GIS. It is suitable for generating continuous surfaces (such as elevation, temperature, and pollutant concentration distribution) from discrete point data. It has the advantages of high computational efficiency, easy implementation, applicability to most continuous data, and more accurate interpolation results in areas with dense data points.

[0045] The results of temporal and spatial dimension analysis are correlated and fused to generate multidimensional analysis results. Correlation fusion uses spatiotemporal indexing technology to establish a spatiotemporal correlation matrix of parameters and identify cross-dimensional anomaly patterns.

[0046] Among them, the formula of the spatiotemporal correlation matrix is ​​expressed as:

[0047] where h (γ, s) denotes the value of the parameter at time γ and location s, γ e and γ v denote different time points, s e and s v denote different location points, cov denotes the covariance, is the standard deviation of the parameter at time γ or location s.

[0048] A spatio-temporal correlation matrix R is constructed, each element of which calculates the Pearson correlation coefficient of the parameter h at different spatio-temporal points and . The standard deviation is normalized as follows:

[0049] where μ e , μ v is the local mean. The spatio-temporal index is used to accelerate the calculation, and when |R| is close to 1, it indicates strong correlation, and deviation identifies abnormal patterns. E is the expectation operator.

[0050] The multi-dimensional analysis results are normalized and aggregated to output standard parameter data. The min-max normalization method is used to map the parameters to the [0, 1] interval, and the sliding window algorithm is used for multi-parameter spatio-temporal feature fusion.

[0051] The intelligent decision module is used to generate intermediate decision instructions according to the standard parameter data. The intelligent decision module includes a rule engine unit, an optimization unit, and a decision generation unit. The rule engine unit is used to evaluate the standard parameter data based on predefined business rules to generate preliminary decision instructions. The predefined business rules include device maintenance threshold rules (such as triggering a cooling instruction when the engine temperature exceeds 85°C), energy consumption warning rules (such as triggering an optimization instruction when the unit earthwork oil consumption exceeds 30%), and environmental compliance rules (such as triggering a work period adjustment when the noise exceeds 70 decibels).

[0052] The optimization unit is used to perform multi-objective optimization on the preliminary decision instructions to generate senior decision instructions. The decision generation unit is used to confirm, conflict resolution, and encapsulation of the senior decision instructions to output intermediate decision instructions. Conflict resolution uses a priority-based negotiation mechanism, and when a device protection instruction conflicts with an efficiency improvement instruction, the device protection instruction is executed first.

[0053] The steps of the rule engine unit generating preliminary decision instructions include: Load the trigger condition and action mapping table of the predefined business rule library. The trigger condition includes the parameter absolute value threshold, the change rate threshold, and the multi-parameter correlation threshold, and the action mapping table stores the condition-action correspondence in XML format.

[0054] Based on the business rule base, the standard parameter data is matched with the trigger condition of the rule. The matching algorithm adopts the Rete pattern matching algorithm, which supports fast matching of complex logical conditions. Rete algorithm is a high-efficiency pattern matching algorithm, mainly used in rule engine system, which can effectively handle the matching problem of a large number of rules and facts. The core idea of Rete algorithm is to avoid repeated calculation by constructing network structure, and its main features include: state saving (remembering part of the past matching results), incremental matching (only recalculating the affected rules), and shared nodes (sharing calculation among common conditions of different rules).

[0055] According to the trigger condition, an initial action instruction set is generated. The initial action instruction set includes device control instructions (such as idle speed adjustment), early warning notification instructions (such as SMS alarm) and data collection instructions (such as encrypted upload).

[0056] According to the initial action instruction set, a preliminary decision instruction is generated.

[0057] The steps of the optimization unit for multi-objective optimization include: The preliminary decision instruction is parsed into a decision variable vector, and a multi-objective function is constructed based on the decision variable vector. The multi-objective function includes an energy consumption minimization function, an efficiency maximization function and a failure rate minimization function. The construction of the multi-objective function is represented as:

[0058]

[0059]

[0060]

[0061] wherein f en represents the energy consumption target function value, f ef represents the efficiency target function value, and f fa represents the failure rate target function value, represents the decision variable vector, such as hydraulic pressure. represents the energy consumption weight coefficient, is the device energy consumption component, such as fuel consumption. t task represents the task execution time, represents the work output indicator, such as earthwork volume, etc. Pr is the failure probability statistical model.

[0062] wherein the multi-objective includes: Energy minimization: total energy consumption of the device weighted sum.

[0063] Efficiency maximization: task completion time t task and work output The negative value ratio (maximizing efficiency is equivalent to minimizing its negative value).

[0064] Failure rate minimization: probability model Pr(fault | x sig ) is regressed based on historical failure data. The objective function is normalized to ensure dimensional consistency and is expressed as:

[0065] where k0 is the target index, k0 ∈ {en, ef, fa}. , from the historical data range. denotes the normalized value of the k0th objective function, denotes the original value of the k0th objective function, denotes the historical minimum value of the k0th objective function, denotes the historical maximum value of the k0th objective function.

[0066] Based on the multi-objective function, the initial instruction set is optimized by a multi-objective genetic algorithm, and solutions with sensitivity higher than the threshold are removed to obtain the preferred instruction set. The multi-objective genetic algorithm adopts the NSGA-III algorithm framework. NSGA-III is the latest improved version of the NSGA series algorithm, and is a multi-objective optimization algorithm based on reference points. NSGA-III retains the basic framework of NSGA-II, but improves the selection mechanism, and the main features include: using a set of predefined reference points to maintain population diversity; solving the problem of inconsistent scales of different objectives; preserving excellent individuals in the non-dominated solution set.

[0067] The process of the multi-objective genetic algorithm (NSGA-III) is as follows: Randomly generate an initial population P0 with a size of N = 100. The individual is represented as a vector r c .

[0068] SBX is used to simulate binary crossover for crossover operation with a probability P c = 0.8, and the formula is expressed as:

[0069] where η is a random factor, i.e., the crossover distribution index, based on the distribution parameter. SBX is a commonly used crossover operator for real-coded genetic algorithms. The main features are: reproducing the search characteristics of binary crossover in real number coding; controlling the distribution of offspring solutions around the parents through parameters; and automatically adjusting the search range according to the distance of parent solutions.

[0070] Mutation operation is performed in a random disturbance manner, expressed as:

[0071] Among them, Δr is the variation disturbance, Δr~N(0,σ m ), σ m is the individual vector standard deviation.

[0072] By assigning ranks through non-dominated sorting, the congestion distance calculation is expressed as:

[0073] The top 20% of the final results are retained as elite solutions.

[0074] In the above formula, N represents the population size, P c is the crossover probability, P m is the mutation probability, d c is the crowding distance, which is used to measure individual sparsity.

[0075] The decision variables of the optimal instruction set are encoded and high-level decision instructions are output. The encoding method adopts a hybrid strategy of real number encoding and binary encoding to ensure efficient representation of continuous and discrete variables.

[0076] like Figure 2 As shown, the step of obtaining the optimal instruction set by the optimization unit through the multi-objective genetic algorithm includes: Initialize the optimization parameters of the multi-objective genetic algorithm and use the decision variable vector as input to construct the initial solution set. The optimization parameters can be optimized by adjusting the maximum number of iterations, the crossover operator, and the mutation operator.

[0077] A multi-objective genetic algorithm is used to iteratively optimize the initial solution set to generate a Pareto front candidate solution set. During the iteration process, the crowding distance is used to maintain the diversity of the solution set, and 20% of the elite solutions are retained in each generation.

[0078] Perform sensitivity analysis on the Pareto front candidate solution set, calculate the sensitivity index of each solution, and generate a sensitivity score. The sensitivity index uses the rate of change of the objective function under a small perturbation of the decision variable and is calculated through Monte Carlo simulation. It can be expressed as:

[0079] Among them, δ m is the random perturbation, f() is the objective function vector, ||·||2 is the L2 norm, S is the sensitivity score, M is the number of Monte Carlo simulations, δ norm is the perturbation normalization factor, usually taken as ||δ m ||2 or a fixed small constant.

[0080] For each solution r on the Pareto front, a small perturbation δ is applied mU(-0.01, 0.01) (uniform distribution). Calculate the target value change after perturbation: L2 norm measure change amplitude. Average the sensitivity score S by M Monte Carlo simulations (e.g. M = 1000). Remove high sensitivity solutions (S > S threshold = 0.15) to ensure robustness. The sensitivity threshold is based on historical decision data, denoted as:

[0081] where μ S, σ S is the historical sensitivity mean and historical sensitivity standard deviation, S threshold is the sensitivity threshold.

[0082] Compare the sensitivity score with the preset sensitivity threshold, remove solutions with sensitivity score higher than the preset sensitivity threshold, and obtain the preferred instruction set. The preset sensitivity threshold is determined by the 3σ rule of historical decision data, usually set to 0.15.

[0083] The decision execution module is used to issue the intermediate decision instruction to the target engineering machinery control system. The decision execution module includes an instruction formatting unit and an instruction issuing unit. The instruction formatting unit is used to convert the intermediate decision instruction into a compatible format of the engineering machinery control system, and outputs the formatted instruction. The compatible format includes CAN bus protocol frame (for chassis control), Modbus protocol message (for hydraulic system) and OPCUA protocol data (for intelligent terminal).

[0084] The instruction issuing unit is used to issue the formatted instruction to the target engineering machinery control system, and process the response feedback and monitor the execution status. The instruction issuing adopts 5G industrial module to realize low latency transmission, and the response timeout threshold is set to 500ms. The execution status is monitored in real time through heartbeat message.

[0085] As shown in Figure 3 , the ecological coordination module is used to interact with the external ecological supervision system and execute the ecological compensation strategy, and update the intermediate decision instruction, and output the final decision instruction. The ecological coordination module includes a data interaction unit, an ecological compensation unit and a decision correction unit. The data interaction unit is used to interact with the external ecological supervision system and send the state data of the engineering machinery, and receive the external ecological data. The data interaction adopts MQTT protocol to realize asynchronous communication, and the transmission data adopts SM4 algorithm of national encryption, and the state data sampling frequency is 1Hz. MQTT is a lightweight publish / subscribe message transmission protocol, designed for low bandwidth, high latency or unreliable network environments, especially suitable for Internet of Things (IoT) application scenarios.

[0086] The ecological compensation unit is used to combine state data and ecological data to generate ecological compensation instructions. The ecological compensation strategy includes: synchronizing the typhoon path of the meteorological bureau and the rainstorm warning, and adjusting the equipment operation period and parking position according to the same; receiving the migration notification of migratory birds and the ecological protection red line data, and delimiting the equipment exclusion area and the limited operation range according to the same; and predicting the demand for building materials according to the equipment operation progress, and automatically placing orders with suppliers to ensure that the material supply is adapted to the requirements of ecological protection.

[0087] The decision correction unit is used to modify the intermediate decision instruction according to the ecological compensation instruction to obtain the final decision instruction.

[0088] The step of modifying the intermediate decision instruction by the strategy updating unit includes: Based on the constraint condition in the ecological compensation instruction, the control parameter in the intermediate decision instruction is dynamically modified. The control parameter modification adopts a proportional regulation algorithm. For example, when the ecological compensation coefficient is L, the energy consumption parameter is multiplied by (1+L) for modification, that is, energy new =energy old ×(1+L). Wherein, energy old is the original energy consumption parameter, energy new is the modified energy consumption parameter, and L is the ecological compensation coefficient. When the ecological compensation coefficient L (for example, L=0.1 corresponds to 10% adjustment), the energy consumption parameter of the intermediate decision instruction is dynamically modified.

[0089] When the external ecological data exceeds the preset threshold, a forced strategy replacement mechanism is activated to generate the final decision instruction.

[0090] A priority mapping rule of the ecological compensation instruction and the intermediate decision instruction is established, and the final decision instruction is output according to the rule conflict resolution result. The priority rule is: ecological red line protection instruction> ecological compensation instruction> equipment operation instruction> efficiency optimization instruction.

[0091] In summary, the engineering machinery Internet of Things intelligent management system provided by the application improves the spatio-temporal correlation analysis accuracy of the device state and the environmental parameters through the ARIMA-GIS hybrid analysis of the spatio-temporal data processing module. Through the intelligent decision module based on the NSGA-III multi-objective genetic algorithm, the Pareto optimal solution is realized among the conflicting targets such as energy consumption, efficiency and failure rate. Through the establishment of the priority mapping rule of the ecological compensation instruction and the intermediate decision instruction, a closed loop of “perception-analysis-decision-execution-feedback” is formed, so that the system can not only optimize the current decision with future data, but also dynamically balance the relationship between construction and ecology. The GIS-based ecological red line dynamic matching algorithm realizes high-precision identification of the operation exclusion boundary, cooperates with the migration cycle data of migratory birds, reduces the interference rate of key ecological areas, and improves the protection effect of biological diversity.

[0092] Those skilled in the art can clearly understand the implementation of the embodiments by means of software and necessary general hardware platforms through the description of the above embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the embodiments or some parts of the embodiments.

[0093] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some technical features thereof; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent management system for the Internet of Things of construction machinery, characterized in that: include: The sensing and acquisition module is used to collect equipment status parameters and environmental parameters of construction machinery in real time; A spatiotemporal data processing module, configured to process, store and analyze the device status parameters and environmental parameters, and output standard parameter data; An intelligent decision-making module, configured to generate intermediate decision-making instructions based on the standard parameter data; A decision execution module, configured to send the intermediate decision instruction to the target engineering machinery control system; The ecological collaboration module is used to interact with the external ecological supervision system for data and to execute the ecological compensation strategy, and to update the intermediate decision instructions and output the final decision instructions.

2. The construction machinery Internet of Things intelligent management system according to claim 1 is characterized in that: The spatiotemporal data processing module includes a data processing unit, a storage unit and an analysis unit; the data processing unit is used to preprocess the device state parameters and environmental parameters to obtain preprocessed fusion parameter data; The storage unit is used to store the pre-processed fusion parameter data to obtain fusion parameter storage data; The analysis unit is used to perform spatiotemporal analysis on the fusion parameter storage data to obtain the standard parameter data.

3. The construction machinery Internet of Things intelligent management system according to claim 2 is characterized in that: The steps of the analysis unit performing spatiotemporal analysis include: Retrieving the stored fusion parameter data to obtain key parameter data; Performing time series analysis and spatial analysis on the key parameter data, and outputting time dimension analysis results and spatial dimension analysis results; Associating and fusing the time dimension analysis result with the space dimension analysis result to generate a multi-dimensional analysis result; The multidimensional analysis results are normalized and aggregated to output the standard parameter data.

4. The construction machinery Internet of Things intelligent management system according to claim 1 is characterized in that: The intelligent decision-making module includes a rule engine unit, an optimization unit and a decision generation unit; the rule engine unit is used to evaluate the standard parameter data based on predefined business rules and generate preliminary decision instructions; the optimization unit is used to perform multi-objective optimization on the preliminary decision instructions and generate advanced decision instructions; the decision generation unit is used to confirm, resolve conflicts and encapsulate the advanced decision instructions and output the intermediate decision instructions.

5. The construction machinery Internet of Things intelligent management system according to claim 4 is characterized in that: The step of generating a preliminary decision instruction by the rule engine unit includes: Load the trigger conditions and action mapping table of the predefined business rule library; Based on the business rule library, matching the standard parameter data with the triggering conditions of the rules; generating an initial action instruction set according to the triggering condition; A preliminary decision instruction is generated according to the initial action instruction set.

6. The construction machinery Internet of Things intelligent management system according to claim 4, characterized in that: The steps of performing multi-objective optimization by the optimization unit include: Parsing the preliminary decision instruction into a decision variable vector, and constructing a multi-objective function based on the decision variable vector; Based on the multi-objective function, an initial instruction set is optimized by a multi-objective genetic algorithm, and solutions with a sensitivity higher than a threshold are eliminated to obtain an optimal instruction set; The decision variables of the preferred instruction set are encoded, and the high-level decision instructions are output.

7. The construction machinery Internet of Things intelligent management system according to claim 6, characterized in that: The step of obtaining the optimal instruction set by the optimization unit through the multi-objective genetic algorithm includes: Initializing the optimization parameters of the multi-objective genetic algorithm and constructing an initial solution set using the decision variable vector as input; executing a multi-objective genetic algorithm to iteratively optimize the initial solution set to generate a Pareto front candidate solution set; Performing sensitivity analysis on the Pareto front candidate solution set, calculating a sensitivity index of each solution, and generating a sensitivity score; The sensitivity score is compared with a preset sensitivity threshold, and solutions having a sensitivity score higher than the preset sensitivity threshold are removed to obtain the preferred instruction set.

8. The construction machinery Internet of Things intelligent management system according to claim 1 is characterized in that: The decision execution module includes an instruction formatting unit and an instruction issuing unit; the instruction formatting unit is used to convert the intermediate decision instruction into a compatible format of the engineering machinery control system and output the formatted instruction; The instruction issuing unit is used to issue the formatted instruction to the target engineering machinery control system, process response feedback and monitor execution status.

9. The construction machinery Internet of Things intelligent management system according to claim 1, characterized in that: The ecological collaboration module includes a data interaction unit, an ecological compensation unit and a decision correction unit; the data interaction unit is used to interact with the external ecological supervision system, send status data of engineering machinery, and receive external ecological data; the ecological compensation unit is used to combine the status data and the external ecological data to generate an ecological compensation instruction; the decision correction unit is used to modify the intermediate decision instruction according to the ecological compensation instruction to obtain the final decision instruction.

10. The construction machinery Internet of Things intelligent management system according to claim 9, characterized in that: The step of the policy updating unit modifying the intermediate decision instruction includes: Dynamically modifying the control parameters in the intermediate decision-making instruction based on the constraints in the ecological compensation instruction; When the external ecological data exceeds a preset threshold, a mandatory policy replacement mechanism is activated to generate a replacement decision instruction; Establish a priority mapping rule between the ecological compensation instruction and the intermediate decision-making instruction, and output the final decision-making instruction based on the rule conflict resolution result.

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