Method and device for evaluating working condition of engineering machinery and engineering vehicle

Abstract

The embodiment of the invention provides a method and device for evaluating the working condition of an engineering machine and an engineering vehicle. The method comprises the steps that the score of working condition data of the engineering machine is obtained; classifying the working condition data according to working condition indexes and operation conditions of the engineering machinery to obtain a plurality of first-class data groups; performing weighted summation on the score of the working condition data in each first-class data group and the corresponding first-class weight to obtain a first-class index group; according to the efficiency, the energy consumption and the reliability of the engineering machinery, classifying the first-class index group to obtain a plurality of second-class data groups; performing weighted summation on the first-level index group in each second-level data group and the corresponding second-level weight to obtain a second-level index group, wherein the second-level index group comprises an efficiency index group, an energy consumption index group and a reliability index group; and performing weighted summation on the second-level index group and the corresponding three types of weights to obtain a third-level index, and determining the working condition of the engineering machinery according to the third-level index. According to the method, the early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of engineering machinery, and in particular to a method and device for evaluating the working condition of engineering machinery and an engineering vehicle. Background Art

[0002] The construction safety and normal operation of construction machinery have always been the main concerns of the society. A comprehensive grasp of the overall operating status of the equipment can achieve early detection and early treatment of equipment failures and abnormalities, avoid causing larger failures and causing irreparable loss of life and property.

[0003] At present, in the concrete pumping machinery industry, there is a lack of evaluation of the overall performance of pumping equipment. Generally, the equipment alarm function is provided in the control program, which can provide alarm prompts when some equipment parameters are abnormal. This method is relatively one-sided in the evaluation of equipment and cannot make a comprehensive evaluation of the health status of the equipment. In addition, the alarm mechanism is an ex post mechanism, and there is a lack of predictive diagnosis mechanism for gray areas such as sub-healthy operation of equipment and operation with illness. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method, a device and a construction vehicle for evaluating the working condition of construction machinery, wherein the method improves the accuracy of early warning.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for evaluating the working condition of a construction machinery, the method comprising:

[0006] Obtaining scores for working condition data of construction machinery;

[0007] Classifying the working condition data according to the working condition index and operation status of the engineering machinery to obtain a plurality of first-class data groups;

[0008] The first-level index group is obtained by weighted summing the scores of the working condition data in each first-class data group and its corresponding first-class weight;

[0009] Classifying the first-level index group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain a plurality of second-level data groups;

[0010] A secondary index group is obtained by weighted summing of the primary index group and its corresponding secondary weight in each secondary data group, wherein the secondary index group includes an efficiency index group, an energy consumption index group and a reliability index group;

[0011] The third-level index is obtained by weighted summing the second-level index group and the three types of weights corresponding to them, and the working condition of the engineering machinery is determined according to the third-level index.

[0012] Optionally, obtaining the scoring of the working condition data of the engineering machinery includes:

[0013] Acquiring the working condition data of the engineering machinery;

[0014] The working condition data are calculated by a scoring membership function to obtain scores corresponding to each working condition data;

[0015] The score membership function includes one or more of a rectangular distribution function, a trapezoidal distribution function, a K-order parabolic distribution function, a Γ-shaped distribution function, a normal distribution function, and a Cauchy distribution function.

[0016] Optionally, the calculating the operating condition data by using a scoring membership function to obtain a score corresponding to each operating condition data includes:

[0017] The working condition data includes first-class data, second-class data and third-class data, wherein the first-class data is data that is evenly distributed within the threshold range and whose value is inversely proportional to the score, the second-class data is data that is within the threshold range and whose value is proportional to the score, and the third-class data is data that is scattered within the threshold range and whose value is inversely proportional to the score;

[0018] Performing a trapezoidal distribution function calculation on the data of the category to obtain a corresponding category of scores;

[0019] Performing Γ-shaped distribution function calculation on the two types of data to obtain the corresponding two types of scores;

[0020] The three types of data are subjected to K-times parabolic distribution function calculations to obtain the corresponding three types of scores.

[0021] Optionally, the working condition data includes travel data, boom speed data, operation energy consumption, travel energy consumption, pipe blocking rate, and boom stability;

[0022] The efficiency index group is the data in the secondary index group that characterizes the efficiency, including: travel arrival data and boom speed data;

[0023] The energy consumption index group is the data representing energy consumption in the secondary index group, including: operating energy consumption and driving energy consumption;

[0024] The reliability index group is data in the secondary index group that characterizes reliability, including: pipe blocking rate and boom stability.

[0025] Optionally, the first type of weight is the contribution of the working condition data in the first type of data group to the first level indicator group;

[0026] The second type of weight is the contribution of the first-level indicator to the second-level indicator group;

[0027] The three types of weights are the contribution of the secondary indicator group to the tertiary indicator.

[0028] Optionally, the evaluation method further includes:

[0029] When the working condition score of the engineering machinery is lower than a threshold range, the working condition position of the engineering machinery is located according to the working condition, and an alarm is triggered.

[0030] On the other hand, the present invention also provides a device for evaluating the working condition of an engineering machinery, the device comprising:

[0031] An acquisition module, used to obtain the scores of the working condition data of the construction machinery;

[0032] A first processing module, used for classifying the working condition data according to the working condition index and operation condition of the engineering machinery to obtain a plurality of first-class data groups;

[0033] The second processing module is used to perform a weighted summation of the scores of the working condition data in each first-class data group and its corresponding first-class weight to obtain a first-class indicator group;

[0034] A third processing module is used to classify the first-level index group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain a plurality of second-class data groups;

[0035] A fourth processing module, for weighted summing the primary index group and its corresponding secondary weight in each secondary data group to obtain a secondary index group, wherein the secondary index group includes an efficiency index group, an energy consumption index group and a reliability index group;

[0036] The fifth processing module is used to obtain a third-level indicator by weighted summing the second-level indicator group and the three types of weights corresponding to them, and determine the working condition of the engineering machinery according to the third-level indicator.

[0037] Optionally, obtaining the scoring of the working condition data of the engineering machinery includes:

[0038] Acquiring the working condition data of the engineering machinery;

[0039] The working condition data are calculated by a scoring membership function to obtain scores corresponding to each working condition data;

[0040] The score membership function includes one or more of a rectangular distribution function, a trapezoidal distribution function, a K-order parabolic distribution function, a Γ-shaped distribution function, a normal distribution function, and a Cauchy distribution function.

[0041] On the other hand, the present invention further provides an engineering vehicle, which includes the above-mentioned device for evaluating the working condition of the engineering machinery.

[0042] On the other hand, the present invention further proposes a readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the method for evaluating the working condition of construction machinery as described above.

[0043] A method for evaluating the working condition of an engineering machinery of the present invention comprises: obtaining the score of the working condition data of the engineering machinery; classifying the working condition data according to the working condition index and operation status of the engineering machinery to obtain multiple first-class data groups; weighted summing the scores of the working condition data in each first-class data group and the corresponding first-class weight to obtain a first-class index group; classifying the first-class index group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain multiple second-class data groups; weighted summing the first-class index group and the corresponding second-class weight in each second-class data group to obtain a second-class index group, wherein the second-class index group includes an efficiency index group, an energy consumption index group and a reliability index group; weighted summing the second-class index group and the corresponding three-class weight to obtain a third-class index, and determining the working condition of the engineering machinery according to the third-class index. The method performs hierarchical analysis and modeling on indicators of different dimensions such as efficiency, energy consumption and reliability, associates the whole machine and indicators of different levels, and provides early warning for indicators of each level and the whole machine, thereby greatly improving the accuracy of early warning.

[0044] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0046] Figure 1 It is a flow chart of a method for evaluating the working condition of engineering machinery of the present invention;

[0047] Figure 2 This is a schematic diagram of pipe blockage rate scoring of the present invention;

[0048] Figure 3 This is an example diagram of the scoring of the 100-trip arrival rate of the present invention;

[0049] Figure 4 is an example diagram of the fuel consumption score of the driving method of the present invention;

[0050] Figure 5 It is a schematic diagram of a system structure of the present invention;

[0051] Figure 6 The invention is a quantitative evaluation system for the working performance of a concrete pump truck. DETAILED DESCRIPTION

[0052] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0053] Figure 1 It is a flow chart of a method for evaluating the working condition of engineering machinery of the present invention, such as Figure 1 As shown, the method includes step S101 for obtaining a score of working condition data of the engineering machinery.

[0054] Specifically, the method of obtaining the score of the working condition data of the engineering machinery includes: obtaining the working condition data of the engineering machinery; calculating the working condition data through a scoring membership function to obtain the score corresponding to each working condition data; the scoring membership function includes one or more of a rectangular distribution function, a trapezoidal distribution function, a K-order parabolic distribution function, a Γ-shaped distribution function, a normal distribution function and a Cauchy distribution function.

[0055] The working condition data includes travel data, boom speed data, operation energy consumption, driving energy consumption, pipe blocking rate, boom stability, etc. The efficiency index group is the data representing efficiency in the secondary index group, including: travel data and boom speed data; the energy consumption index group is the data representing energy consumption in the secondary index group, including: operation energy consumption and driving energy consumption; the reliability index group is the data representing reliability in the secondary index group, including: pipe blocking rate and boom stability.

[0056] According to a specific implementation manner, the operating condition data is calculated through a scoring membership function to obtain scores corresponding to each operating condition data, including: the operating condition data includes one category of data, two categories of data and three categories of data, the one category of data is data that is evenly distributed within a threshold range and whose values ​​are inversely proportional to the scores, the two categories of data is data that is within the threshold range and whose values ​​are directly proportional to the scores, and the three categories of data is data that is scattered within the threshold range and whose values ​​are inversely proportional to the scores; the one category of data is calculated using a trapezoidal distribution function to obtain its corresponding one category of scores; the two category of data is calculated using a Γ distribution function to obtain its corresponding two category scores; the three category of data is calculated using a K-fold parabolic distribution function to obtain its corresponding three category scores.

[0057]

[0058] Among them, f(x) is the scoring membership function, x is the average of the arithmetic mean and arithmetic median values ​​of the working condition indicators within a period of time (depending on the evaluation time range of the scoring function, such as the working condition indicators within one day if the evaluation is one day, and the working condition indicators within one month if the evaluation is one month, which generally does not exceed one month), and the scoring result is a scoring value in the range of [0,1]. k1, k2, k3 are the parameters of the three-level indicator evaluation function, which can be determined by the statistics of the working condition indicators of the same type of equipment, and it is necessary to ensure that the piecewise function is continuous. e is a natural constant.

[0059] The selection of the score membership function includes but is not limited to a rectangular distribution function, a trapezoidal distribution function, a K-order parabolic distribution function, a Γ-shaped distribution function, a normal distribution function, a Cauchy distribution function, and the like.

[0060] The trapezoidal distribution function is:

[0061]

[0062] Figure 2 Schematic diagram of pipe blocking rate scoring of the present invention, as shown in Figure 2 As shown, the trapezoidal distribution function is suitable for the pipe blocking rate, overpressure rate and other conditions with cutoff thresholds, and the smaller the value, the higher the score. The values ​​of each operating condition are within the definition domain and are distributed relatively evenly, and there is no need to make a clear distinction between the three-level indicators for the data within a certain range.

[0063] Γ distribution function + trapezoidal distribution includes:

[0064]

[0065] Figure 3 This is an example diagram of the 100-trip arrival rate scoring of the present invention, such as Figure 3 As shown, the Γ distribution function and trapezoidal distribution are applicable to the 100-stroke in-place rate, cumulative stroke in-place rate and other non-cutoff thresholds, and the larger the value, the higher the score. The values ​​of various working conditions are very unevenly distributed within the definition domain, and it is necessary to make a clear distinction between the data within a certain range of the third-level indicators (for example, most of the data on the 100-stroke in-place rate of each device are concentrated in the range of 90-95, so it is necessary to make a greater distinction between the data within this range and set a larger change rate).

[0066] k-order parabolic distribution + trapezoidal distribution includes:

[0067]

[0068] like Figure 4As shown in the figure, the k-order parabolic distribution and trapezoidal distribution are suitable for pumping energy consumption, idling energy consumption, boom energy consumption, driving energy consumption and other conditions with cutoff thresholds, and the smaller the value, the higher the score. The values ​​of various working conditions are relatively unevenly distributed within the definition domain, and it is necessary to make a certain distinction between the data within a certain range of the third-level indicators.

[0069] The present invention adopts different scoring algorithms according to the conditions of the different working condition data of the engineering machinery, and accurately refines the scores of the different working condition data, which is convenient for precise positioning in the later stage.

[0070] Step S102 is to classify the working condition data according to the working condition indicators and operation conditions of the engineering machinery to obtain a plurality of first-class data groups.

[0071] According to a specific implementation mode, Figure 5 As shown, the one type of data group can be a stroke arrival rate data group, a boom speed data group, a pumping efficiency data group and other data groups that can characterize efficiency, an operating energy consumption data group, a driving energy consumption data group, a pipe blockage rate data group, a boom stability data group and other data groups that can characterize reliability.

[0072] Specifically, the stroke in-place rate data group includes the stroke in-place rate of 100 times and the cumulative stroke in-place rate, etc.; the boom speed data group includes the rotation speed, arm movement speed, etc.; the operating energy consumption data group includes pumping energy consumption, idling energy consumption, boom energy consumption, etc.; the driving energy consumption data group includes driving energy consumption, etc.; the pipe blockage rate data group includes pipe blockage rate, overpressure rate, etc.; the boom stability data group includes boom end fluctuation, arm force difference fluctuation, etc.

[0073] This method subdivides the working condition data according to working condition indicators and operating conditions, which facilitates the subsequent accurate and rapid positioning of specific working condition data.

[0074] Step S103 is to obtain a primary index group by weighted summing the score of the working condition data in each first-class data group and its corresponding first-class weight. The first-class weight is the contribution of the working condition data in the first-class data group to the first-class index group.

[0075]

[0076] Among them, U ijk is the score of each working condition data, which is calculated by the actual working condition value of the score and the score membership function f(x), and its value is a score in the range of [0,1].

[0077] The score membership function f(x) is determined by the distribution shape and characteristics of the historical data of each underlying working condition, and is finally confirmed in combination with the mechanism model and working experience. ijkis a type of weight, which is the contribution of the operating condition data in a type of data group to the primary indicator group. This value is determined based on the mechanism model and empirical data.

[0078] According to a specific implementation, the first-level index group includes a travel arrival rate index, a boom speed index, an operation energy consumption index, a travel energy consumption index, a pipe blocking rate index, a boom stability index, etc. Figure 5 As shown, the stroke in-place rate data group includes the stroke in-place rate of 100 times and the cumulative stroke in-place rate, etc.; the boom speed data group includes the rotation speed, arm movement speed, etc.; the operation energy consumption data group includes pumping energy consumption, idling energy consumption, boom energy consumption, etc.; the driving energy consumption data group includes driving energy consumption, etc.; the pipe blocking rate data group includes the pipe blocking rate, overpressure rate, etc.; the boom stability data group includes boom end fluctuation, arm force difference fluctuation, etc. The stroke in-place rate index is obtained by weighted summing the scores of the stroke in-place rate of 100 times and the cumulative stroke in-place rate and their corresponding weights; the stroke to boom speed index is obtained by weighted summing the rotation speed and 1-N arm movement speed and their corresponding weights.

[0079] This method realizes the quantification of specific working condition data, which is convenient for subsequent management.

[0080] Step S104 is to classify the first-level indicator group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain a plurality of second-level data groups.

[0081] According to a specific implementation manner, the first-level indicator group includes a stroke arrival rate indicator, a boom speed indicator, an operation energy consumption indicator, a driving energy consumption indicator, a pipe blockage rate indicator, a boom stability indicator, and the like.

[0082] The two data groups used to characterize the efficiency of the engineering machinery include a stroke arrival rate index and a boom speed index; the two data groups used to characterize the energy consumption of the engineering machinery include an operating energy consumption index and a driving energy consumption index; the two data groups used to characterize the reliability of the engineering machinery include a pipe blockage rate index and a boom stability index.

[0083] Step S105 is to obtain a secondary index group by weighted summing of the primary index group and its corresponding secondary weight in each secondary data group, wherein the secondary index group includes an efficiency index group, an energy consumption index group and a reliability index group. The secondary weight is the contribution of the primary index to the secondary index group.

[0084]

[0085] Among them, U i is the secondary indicator group, w ij is the second-class weight, which is determined based on the mechanism model and empirical data. ij It is a first-level indicator group.

[0086] According to a specific implementation, the efficiency index group is a two-type data group used to characterize the efficiency of the engineering machinery, including a stroke arrival rate index and a boom speed index. The energy consumption index group is a two-type data group used to characterize the energy consumption of the engineering machinery, including an operating energy consumption index and a driving energy consumption index. The reliability index group is a two-type data group used to characterize the reliability of the engineering machinery, including a pipe blocking rate index and a boom stability index.

[0087] An efficiency index group is obtained by weighted summation of the stroke arrival rate index and the boom speed index and their corresponding two types of weights; an energy consumption index group is obtained by weighted summation of the operation energy consumption index and the travel energy consumption index and their corresponding two types of weights; a reliability index group is obtained by weighted summation of the pipe blockage rate index and the boom stability index and their corresponding two types of weights.

[0088] The present invention realizes a comprehensive quantitative evaluation of the overall working performance and usage of concrete engineering machinery based on evaluation indicators of different dimensions such as energy consumption, efficiency, and reliability.

[0089] Step S106 is to obtain a third-level indicator by weighted summing the second-level indicator group and its corresponding three types of weights, and determine the working condition of the engineering machinery according to the third-level indicator. The three types of weights are the contribution of the second-level indicator group to the third-level indicator.

[0090] According to a preferred embodiment, the secondary index group includes an efficiency index group, an energy consumption index group and a reliability index group. The efficiency index group, the energy consumption index group and the reliability index group and their corresponding three types of weights are weighted and summed to obtain a third-level index. The working condition of the engineering machinery is determined according to the third-level index. When the working condition score of the engineering machinery is lower than a threshold range, the working condition position of the engineering machinery is located according to the working condition, and an alarm is triggered.

[0091] Figure 6 It is a quantitative evaluation system for the working performance of a concrete pump truck. Figure 6 As shown, the system mainly includes the following three units:

[0092] Control unit: converts the collected relevant sensor signals and switch signals into corresponding digital signals.

[0093] Calculation unit: Based on relevant digital signals, it calculates the operating conditions required for quantitative evaluation of the working performance of a single device, such as overpressure rate, pumping energy consumption, driving energy consumption, idling energy consumption, etc.

[0094] Wireless transmission unit: uploads the digital signals transmitted from the control unit and the computing unit to the cloud platform according to certain coding rules.

[0095] The control unit converts the collected sensor signals such as boom vibration amplitude, boom movement speed, stroke completion rate and switch signals such as starting pumping into corresponding digital signals, and then obtains the stroke completion rate, boom vibration amplitude, idle system fuel consumption, driving system fuel consumption, pumping system fuel consumption, pipe blocking rate, overpressure rate and other data through the calculation unit, and transmits them to the cloud platform through the wireless transmission unit. The data warehouse model on the cloud platform automatically performs pre-processing such as removing outliers and filling interpolation on the uploaded original working condition data at each threshold time interval. The processed data is evaluated by the quantitative evaluation model to calculate the performance score of the equipment over a period of time.

[0096] Based on this evaluation system, the equipment with a cluster score lower than the first scoring threshold, a single device overall score lower than the second scoring threshold, and a single indicator score lower than the third scoring threshold is identified, and the poorly performing clusters, equipment, and subsystems are quickly located to assist users in technical reform and tracking. The first scoring threshold, the second scoring threshold, and the third scoring threshold are set based on experience.

[0097] The present invention systematically evaluates the performance of the operating machinery based on its overall working condition, obtains working condition data in real time through a data warehouse, establishes a quantitative evaluation model based on the working condition data, and monitors the equipment score of the operating machinery in real time. When the performance evaluation score of the engineering machinery is lower than a certain threshold over a period of time, an abnormal alarm is triggered. At the same time, the upper-level indicator system score of the system is based on the characteristics obtained by weighted sum of the scores of the lower-level indicator systems. It can quickly locate the subsystem that affects the equipment score, accurately locate the reason for the low score of the equipment, guide relevant personnel to make adjustments and technical improvements, and drive product improvements. The present invention can automatically identify engineering machinery in a sub-healthy state, and can further analyze and mine the reasons for the abnormal scores of engineering machinery based on the identification results, provide guidance for technical improvements, and drive product improvements.

[0098] On the other hand, the present invention also proposes a device for evaluating the working condition of engineering machinery, which includes: an acquisition module for acquiring a score of the working condition data of the engineering machinery; a first processing module for classifying the working condition data according to the working condition indicators and operating conditions of the engineering machinery to obtain multiple first-class data groups; a second processing module for weighted summing up the score of the working condition data in each first-class data group and its corresponding first-class weight to obtain a first-class indicator group; a third processing module for classifying the first-class indicator group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain multiple second-class data groups; a fourth processing module for weighted summing up the first-class indicator group and its corresponding second-class weight in each second-class data group to obtain a second-class indicator group, wherein the second-class indicator group includes an efficiency indicator group, an energy consumption indicator group and a reliability indicator group; a fifth processing module for weighted summing up the second-class indicator group and its corresponding three-class weights to obtain a third-class indicator, and determining the working condition of the engineering machinery according to the third-class indicator.

[0099] The method of obtaining the scoring of the working condition data of the engineering machinery includes: obtaining the working condition data of the engineering machinery; calculating the working condition data through a scoring membership function to obtain the scores corresponding to each working condition data; the scoring membership function includes one or more of a rectangular distribution function, a trapezoidal distribution function, a K-order parabolic distribution function, a Γ-shaped distribution function, a normal distribution function and a Cauchy distribution function.

[0100] This device can not only grasp the overall operation of the equipment through the performance scoring of the engineering machinery, but also understand the operation status of each subsystem and achieve precise positioning. The scoring system can realize automatic identification and early warning of the sub-health status of the equipment, analyze and diagnose the reasons for the sub-health of the equipment, and finally accurately locate the abnormal problems of the equipment, guide the rapid handling of the problems, reduce equipment downtime, and improve equipment utilization. Based on the quantitative evaluation model of the performance of a single device, a quantitative evaluation model of the performance of the equipment fleet is established. Based on this quantitative evaluation model of the fleet, it is possible to quantitatively score the equipment fleets in different regions / product configurations / customer groups, thereby driving product research and development improvements and differentiated marketing.

[0101] On the other hand, the present invention further provides an engineering vehicle, which includes the above-mentioned device for evaluating the working condition of the engineering machinery.

[0102] On the other hand, the present invention further proposes a readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the method for evaluating the working condition of construction machinery as described above.

[0103] A method for evaluating the working condition of an engineering machinery of the present invention comprises: obtaining the score of the working condition data of the engineering machinery; classifying the working condition data according to the working condition index and operation status of the engineering machinery to obtain multiple first-class data groups; weighted summing the scores of the working condition data in each first-class data group and the corresponding first-class weight to obtain a first-class index group; classifying the first-class index group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain multiple second-class data groups; weighted summing the first-class index group and the corresponding second-class weight in each second-class data group to obtain a second-class index group, wherein the second-class index group includes an efficiency index group, an energy consumption index group and a reliability index group; weighted summing the second-class index group and the corresponding three-class weight to obtain a third-class index, and determining the working condition of the engineering machinery according to the third-class index. The method performs hierarchical analysis and modeling on indicators of different dimensions such as efficiency, energy consumption and reliability, associates the whole machine and indicators of different levels, and provides early warning for indicators of each level and the whole machine, thereby greatly improving the accuracy of early warning.

[0104] The present invention can not only provide an alarm mechanism for a single indicator of the pumping equipment, but also associate the whole machine and indicators at different levels by performing hierarchical analysis and modeling on indicators of different dimensions such as efficiency, energy consumption, and reliability, so as to provide early warnings for indicators at each level and the whole machine, thereby greatly improving the accuracy of the early warning. By providing an alarm mechanism in the form of scoring when the equipment or subsystem is sub-healthy, the equipment or subsystem in a sub-healthy state can be identified in advance, and the long-term operation of the equipment with illness can be avoided, which greatly improves the real-time nature of the early warning. The present invention can also compare the advantages and disadvantages of the use of different fleets of equipment outside (such as all equipment of a certain model, all equipment in a certain area, and all equipment of a certain customer), and assist users in comparing the use of different products outside.

[0105] The optional implementation modes of the embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation modes. Within the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical scheme of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

[0106] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0107] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a single-chip microcomputer, a chip or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0108] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for evaluating the working condition of construction machinery, characterized in that: The method includes: Obtaining scores for working condition data of construction machinery; Classifying the working condition data according to the working condition index and operation status of the engineering machinery to obtain a plurality of first-class data groups; The first-level index group is obtained by weighted summing the scores of the working condition data in each first-class data group and its corresponding first-class weight; Classifying the first-level index group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain a plurality of second-level data groups; A secondary index group is obtained by weighted summing of the primary index group and its corresponding secondary weight in each secondary data group, wherein the secondary index group includes an efficiency index group, an energy consumption index group and a reliability index group; The third-level index is obtained by weighted summing the second-level index group and the three types of weights corresponding to them, and the working condition of the engineering machinery is determined according to the third-level index.

2. The method according to claim 1, characterized in that: The step of obtaining the scoring of the working condition data of the construction machinery includes: Acquiring the working condition data of the engineering machinery; The working condition data are calculated by a scoring membership function to obtain scores corresponding to each working condition data; The score membership function includes one or more of a rectangular distribution function, a trapezoidal distribution function, a K-order parabolic distribution function, a Γ-shaped distribution function, a normal distribution function, and a Cauchy distribution function.

3. The method according to claim 2, characterized in that The step of calculating the operating condition data by using a scoring membership function to obtain scores corresponding to each operating condition data includes: The working condition data includes first-class data, second-class data and third-class data, wherein the first-class data is data that is evenly distributed within the threshold range and whose value is inversely proportional to the score, the second-class data is data that is within the threshold range and whose value is proportional to the score, and the third-class data is data that is scattered within the threshold range and whose value is inversely proportional to the score; Performing a trapezoidal distribution function calculation on the data of the category to obtain a corresponding category of scores; Performing Γ-shaped distribution function calculation on the two types of data to obtain the corresponding two types of scores; The three types of data are subjected to K-times parabolic distribution function calculations to obtain the corresponding three types of scores.

4. The method according to claim 1, characterized in that: The working condition data include travel data, boom speed data, operation energy consumption, travel energy consumption, pipe blocking rate, and boom stability; The efficiency index group is the data in the secondary index group that characterizes the efficiency, including: travel arrival data and boom speed data; The energy consumption index group is the data representing energy consumption in the secondary index group, including: operating energy consumption and driving energy consumption; The reliability index group is data in the secondary index group that characterizes reliability, including: pipe blocking rate and boom stability.

5. The method according to claim 1, characterized in that The first type of weight is the contribution of the working condition data in the first type of data group to the first level index group; The second type of weight is the contribution of the first-level indicator to the second-level indicator group; The three types of weights are the contribution of the secondary indicator group to the tertiary indicator.

6. The method according to claim 1, characterized in that The evaluation method also includes: When the working condition score of the engineering machinery is lower than a threshold range, the working condition position of the engineering machinery is located according to the working condition, and an alarm is triggered.

7. A device for evaluating the working condition of construction machinery, characterized in that: The device includes: An acquisition module, used to obtain the scores of the working condition data of the construction machinery; A first processing module, used for classifying the working condition data according to the working condition index and operation condition of the engineering machinery to obtain a plurality of first-class data groups; The second processing module is used to perform a weighted summation of the scores of the working condition data in each first-class data group and its corresponding first-class weight to obtain a first-class indicator group; A third processing module is used to classify the first-level index group according to the efficiency, energy consumption and reliability of the engineering machinery to obtain a plurality of second-class data groups; A fourth processing module, for weighted summing the primary index group and its corresponding secondary weight in each secondary data group to obtain a secondary index group, wherein the secondary index group includes an efficiency index group, an energy consumption index group and a reliability index group; The fifth processing module is used to obtain a third-level indicator by weighted summing the second-level indicator group and the three types of weights corresponding to them, and determine the working condition of the engineering machinery according to the third-level indicator.

8. The device according to claim 7, characterized in that The step of obtaining the scoring of the working condition data of the construction machinery includes: Acquiring the working condition data of the engineering machinery; The working condition data are calculated by a scoring membership function to obtain scores corresponding to each working condition data; The score membership function includes one or more of a rectangular distribution function, a trapezoidal distribution function, a K-order parabolic distribution function, a Γ-shaped distribution function, a normal distribution function, and a Cauchy distribution function.

9. An engineering vehicle, characterized in that: The engineering vehicle comprises the device for evaluating the working condition of the engineering machinery as described in claim 7 or 8 above.

10. A readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the method for evaluating the working condition of a construction machine according to any one of claims 1 to 6.

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