Engineering mechanical equipment calling method and system based on big data

Abstract

The invention relates to the technical field of information and communication technologies specially suitable for management or supervision, in particular to an engineering mechanical equipment calling method and system based on big data, and the method comprises the steps: obtaining a demand order, determining a longest calling route, executing callable equipment based on a target mechanical equipment library, and determining callable allowance. Obtaining specific information of callable equipment; determining a calling result of the callable equipment based on the longest route; and obtaining an optimal strategy based on a calling result. According to the method and the device, the called mechanical equipment is accurately determined, the error probability of selecting and calling the mechanical equipment is reduced, and the mechanical equipment is accurately and clearly called.

Description

Technical Field

[0001] The present application relates to the technical field of information and communication technology specifically suitable for management or supervision purposes, and in particular to a method for calling engineering machinery equipment based on big data, and also to a system. Background Art

[0002] The rapid development of various industries today has led to a large increase in the types and quantity of construction machinery equipment. Generally speaking, the types of construction machinery include excavators, earth-moving machinery, engineering lifting machinery, industrial vehicles, compaction machinery, piling machinery, concrete machinery, steel bars and prestressed machines, etc. When it is necessary to deploy and use construction machinery equipment, due to the wide variety and complex usage time, it brings about the problem of difficulty in the management of construction machinery equipment.

[0003] The current publication date is May 17, 2022, the publication number is CN114511219A, and the name is a Chinese invention application for a management system of intelligent engineering machinery equipment based on big data and its implementation method. It discloses that at least one engineering machinery equipment, at least one mobile terminal, and a system server are connected to each other to manage the engineering machinery equipment; various types of equipment information are collected on the engineering machinery equipment for analysis by the system server and viewing by the terminal device; the mobile terminal is wirelessly connected to the system server to submit various lists to manage the engineering machinery equipment; the system server is equipped with an engineering machinery equipment management system; the engineering machinery equipment management system is used to analyze the historical situation of equipment leasing and market demand, confirm the current equipment distribution situation, and regionally transfer and transport individual engineering machinery equipment.

[0004] The aforementioned technologies make the control of construction machinery and equipment more direct and convenient, and improve the management efficiency of managers. However, there are still difficulties when it comes to the need for rational allocation and use of machinery and equipment. Summary of the invention

[0005] Through research, the inventors found that the deployment and use of construction machinery and equipment is extremely difficult due to the diversity and complexity of construction machinery and equipment. Traditional manual management will result in a failure to fully and clearly report relevant information to the management personnel after the equipment is called, causing relevant personnel to misjudge the latest status of the equipment, further causing delays in the construction schedule of other subsequent construction sites that need to use the corresponding machinery and equipment, resulting in economic losses.

[0006] The purpose of the present application is to provide a method and system for invoking construction machinery and equipment based on big data. By means of the longest invocation route based on the demand order, combined with the optimal strategy and supervised learning, the final invocation result is obtained to solve the technical problem that the prior art cannot provide a method that can accurately and clearly implement the invocation of mechanical equipment.

[0007] According to one aspect of the present application, a method for invoking construction machinery and equipment based on big data is provided. The method is executed by a processor, and the method includes:

[0008] Connect to the information database of the target allocation transportation front end to obtain a demand order, and determine the longest invocation route based on the invocation type and invocation destination in the demand order, where the invocation type is the specified invoked equipment, and the invocation destination is the specified invocation end point;

[0009] Based on the target mechanical equipment database, perform the determination of invokable equipment and invokable margin to obtain the specific information of the invokable equipment;

[0010] Determine the invocation result of the invokable equipment based on the longest route;

[0011] Based on the invocation result, perform a matching decision, sort the matching decision result, and obtain the optimal strategy;

[0012] Generate mechanical equipment that meets the demand order based on the optimal strategy, and perform supervised learning to obtain the final invocation method.

[0013] In some embodiments, the process of connecting to the information database of the target allocation transportation front end to obtain a demand order and determining the longest invocation route based on the invocation type and invocation destination in the demand order includes:

[0014] According to the demand order received in the information database, obtain the detailed demand content, at least including the order time, order originator, and order purpose, where the order purpose at least includes the invocation type and invocation destination;

[0015] Identify and determine the longest invocation route to the same destination in the past according to the invocation destination.

[0016] In some embodiments, based on the target mechanical equipment database, perform the determination of invokable equipment and invokable margin to obtain the specific information of the invokable equipment, at least including: according to the mechanical equipment database corresponding to the receiving address of the demand order, determine the equipment types that can be invoked in the mechanical equipment database and the corresponding quantities of the equipment that can be invoked.

[0017] In some embodiments, determining the invocation result of the invokable equipment based on the longest route includes at least:

[0018] Determine the type of mechanical equipment that meets the route information according to the longest route information;

[0019] According to the type of mechanical equipment that meets the longest route information, use a sorting algorithm to obtain the quantity sorting of the corresponding mechanical equipment, determine the screening value for the sorting result, obtain the screening sorting result from more to less in quantity, and determine the matching degree between the equipment type corresponding to the screening sorting result and the previous equipment type corresponding to the longest route;

[0020] Based on the matching degree determination result and combined with the deduction algorithm, determine the impact rate on the remaining construction period and the call waiting duration after calling the mechanical equipment with qualified matching degree, and obtain the call result.

[0021] In some embodiments, based on the call result, perform a matching decision, sort the matching decision result, and obtain the optimal strategy, including at least:

[0022] Perform re-deduction on the mechanical equipment that affects the construction period, and determine the call waiting duration for the mechanical equipment that does not affect the construction period;

[0023] For the mechanical equipment with a call waiting duration greater than or equal to 1 day, perform waiting, and for the mechanical equipment with a call waiting duration less than 1 day, perform call waiting;

[0024] Sort the call waiting objects to obtain the most preferred selection object, and the most preferred selection object is the mechanical equipment with the shortest call waiting duration.

[0025] In some embodiments, generate mechanical equipment that meets the demand order based on the optimal strategy, and perform supervised learning to obtain the final call method, including at least:

[0026] Perform supervised learning on the mechanical equipment with the shortest call waiting duration to obtain the mechanical equipment with a call waiting time error lower than 10% and greater than or equal to 10%; determine the call for the mechanical equipment with an error lower than 10%.

[0027] According to another aspect of the present application, a call system for construction machinery equipment based on big data is provided. The system includes a processor, and further includes:

[0028] An external connection module, which is used to connect to the information library at the front end of the target allocation transportation to obtain a demand order, and determine the call longest route based on the call type and call destination in the demand order;

[0029] A call determination module, which based on the target mechanical equipment library, determines the callable equipment and the callable margin, and obtains the specific information of the callable equipment; determines the call result of the callable equipment based on the longest route;

[0030] The decision-making module makes a matching decision based on the call result, sorts the matching decision result, and obtains the optimal strategy.

[0031] The result generation module generates mechanical equipment that meets the demand order based on the optimal strategy, and performs supervised learning to obtain the final call method.

[0032] In some embodiments, the processor is data-connected to the external connection module, the call determination module, the decision-making module, and the result generation module.

[0033] According to the technical solution of the present application, the achievable beneficial effects are as follows:

[0034] Determine the longest call route based on the call type and call destination of the demand order, realize the simulation of extreme call environments, improve the adaptability of the call method during long-distance calls, and enhance the call accuracy under extreme conditions; determine the call result of the callable equipment based on the longest route, make a matching decision, sort the matching decision result, obtain the optimal strategy, generate mechanical equipment that meets the demand order based on the optimal strategy, and perform supervised learning to obtain the final call method. Based on this, accurately determine the call of mechanical equipment, reduce the probability of incorrectly selecting the mechanical equipment to be called, and achieve accurate and clear implementation of the call of mechanical equipment. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0036] Figure 1 It is the flowchart of the call method of the present application;

[0037] Figure 2 It is the structural diagram of the call system of the present application;

[0038] Figure 3 It is the usage scenario diagram of the call method of the present application. Detailed Embodiments

[0039] The following will combine the drawings in the embodiments of the present application Figures 1-3 to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments.

[0040] The terms used in the specification are for describing embodiments and do not limit the present application. Unless otherwise defined, all terms used in this specification shall have the same meaning as commonly understood by those skilled in the art to which this application pertains.

[0041] Overview of the Application

[0042] There is a wide variety of construction machinery with complex and diverse application scenarios, which brings quite a lot of trouble to the management of construction machinery equipment and also causes difficulties for the management of construction machinery equipment leasing companies. At the same time, due to the relatively high flexibility of mechanical equipment in use, the calling of mechanical equipment is often random or at any time. Therefore, if the calling of mechanical equipment is not accurately and clearly managed, it will cause calling chaos, which will in turn affect the use and result in waste of resources in many aspects such as manpower and material resources.

[0043] Based on the foregoing, this application is to ensure accurate calling, perform calling simulations under extreme conditions, and cooperate with the optimal strategy to accurately obtain the corresponding construction machinery equipment, reduce resource waste, and improve the economic benefits of construction machinery calling.

[0044] Embodiment 1

[0045] The embodiment of this application provides a method for calling construction machinery equipment based on big data, which is executed by a processor. The so-called processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0046] Further, in combination with Figure 1 The flowchart of the method for calling construction machinery equipment based on big data, the method includes:

[0047] Connect to the information database of the target assigned transportation front end to obtain a demand order, and determine the longest calling route based on the calling type and calling destination in the demand order. Among them, the calling type is the specified calling equipment, and the calling destination is the specified calling end point. It can be understood that the demand order includes the order time, order initiator, and order purpose. Among them, the order purpose includes at least the calling type and calling destination. According to the calling destination, identify and determine the longest calling route for the same destination in the past.

[0048] The information database refers to the obtained order summary database, which is used for unified management of orders. Generally, it includes the order originator, order time, order amount, order type, etc. The target allocation transportation front end refers to the order source in the information database, that is, the third-party order. The third-party order will enter the relevant ordered mechanical equipment library through the information database for execution and determination. The call type and call destination in the demand order will clearly inform the name of the mechanical equipment to be called and the specific location of the call. For example, Order A requires 2 single-arm cranes to be called to Area M for 3 days. At this time, the mechanical equipment library will determine the call information, and then determine the longest route to the call destination. Determining the longest route at this time is to prepare for extreme situations. When there are road conditions, personnel, or route problems during the call, it will not affect the subsequent use of the equipment, and at the same time, it can ensure the timeliness of long-distance calls. Therefore, selecting the farthest call distance not only issues a risk warning but also maximizes the economic benefits when the route has problems.

[0049] Based on the target mechanical equipment library, determine the callable equipment and the callable margin, and obtain the specific information of the callable equipment. In some possible implementation manners, according to the mechanical equipment library corresponding to the receiving address of the demand order, determine the types of equipment that can be called in the mechanical equipment library and the corresponding quantities of the callable equipment.

[0050] The receiving address of the demand order is a factory building or company where mechanical equipment can be called. After receiving the order, it will confirm the order information. This process can be either manual operation or machine-assisted. To ensure real-time reception, generally, the computer reception method is used. After the order information is confirmed, it will confirm the information of the equipment required by the order. The confirmation content is whether there is such mechanical equipment and the current quantity of the mechanical equipment. The quantity includes the equipment quantity that has been ordered, not ordered, and ordered but has no impact on this order. For example, Order A requires 2 single-arm cranes to be called to Area M for 3 days. At this time, the single-arm crane is determined. After determining that the equipment has not been called, determine whether the equipment has been ordered and simultaneously determine whether it interferes with this order. If there is no interference, obtain the specific information of the corresponding equipment and execute the call for standby. It should be noted that the specific information here refers to the characterization attribute information such as the model, size, and weight of the equipment.

[0051] Determine the call result of the callable equipment based on the longest route. In some possible implementation manners, according to the longest route information, determine the type of mechanical equipment that meets the route information; according to the type of mechanical equipment that meets the longest route information, use a sorting algorithm to obtain the quantity sorting of the corresponding mechanical equipment, determine the screening value for the sorting result, obtain the screening sorting result from more to less in quantity, and determine the matching degree between the equipment type corresponding to the screening sorting result and the previous equipment type corresponding to the longest route; based on the matching degree determination result and combined with the deduction algorithm, determine the influence rate on the remaining construction period and the call waiting duration after calling the mechanical equipment with qualified matching degree, and obtain the call result.

[0052] Determine the corresponding longest call route, and determine the matching degree of the foregoing corresponding mechanical equipment to this longest route. First, obtain the previous call route of this mechanical equipment, determine the longest call distance based on the previous call route, compare the longest call distance with the foregoing longest call distance, and the distance difference within 3 kilometers to 5 kilometers can meet the longest call route. Then, use the bubble sort to sort the quantity of the corresponding equipment that meets the foregoing longest route. It should be noted that since there is generally more than one or one type of equipment that meets this route, it is necessary to sort the quantity. For example, the corresponding equipment that meets the non-interference is a single-arm single-angle crane, a single-arm multi-angle crane, a single-arm small-weight crane, and a single-arm large-weight crane. At this time, it is necessary to determine the equipment that meets the longest call route for the foregoing 4 types of single-arm cranes. At this time, the unqualified single-arm cranes will be screened out, and the remaining ones will be sorted in quantity, that is, a bubble sort from more to less in quantity. At this time, from an economic perspective, the single-arm crane with the largest quantity will be called first, and so on.

[0053] Next, the matching degree of the previous equipment type corresponding to the longest route will be determined, and the single-girder crane screened out above will be matched with the single-girder crane used on this route in the past. At this time, two situations will occur. One is that there is a corresponding call for the single-girder crane on this route in the past, and then a single-girder crane with the same model or the same function can be directly selected; the other is that there is no corresponding single-girder crane on this route in the past. At this time, the single-girder crane with the largest sorting quantity is called and waited. Immediately afterwards, based on the matching degree determination result, after the mechanical equipment with qualified matching degree is called using the Monte Carlo method, the impact rate on the construction period of other construction sites that need to use this equipment and the call waiting time are determined. Specifically, it is traversed whether there are other orders during the demand order time of this equipment machinery. If so, the specific order information is checked to determine whether it is the same equipment. For example, the single-arm single-angle crane is the mechanical equipment to be called that has been determined. At this time, there is another order B that also needs to call this single-arm single-angle crane. At this time, it is necessary to further determine whether it is the same order, that is, to determine by model or number. If so, it is arranged according to the order placement sequence, that is, the order placed first is given priority, and the other order matches the remaining equipment. Because the two have the same function and the only difference is in the model or number, it can be realized. Finally, according to the usage time of the demand order, the call waiting time is determined. If the call waiting time is relatively long and it will affect the order usage time, then another one is called. If it will not affect the order usage time, then it is called in line. It should be noted that the call waiting time in this implementation is the preparation time of the corresponding equipment machinery before being called. It should be noted that there will not be a situation where all called equipment will affect the order, which is also a common problem that actual companies or factories need to avoid.

[0054] Based on the call result, a matching decision is executed, and the matching decision result is sorted to obtain the optimal strategy. In some possible implementation manners, the mechanical equipment that affects the construction period is deduced again using the Monte Carlo method, and the mechanical equipment that has no impact on the construction period is used to determine the call waiting time. Among them, preferably in this implementation, for the mechanical equipment with a call waiting time greater than or equal to 1 day, waiting is executed, and for the mechanical equipment with a call waiting time less than 1 day, call waiting is executed; the call waiting objects are sorted to obtain the most preferred selection object, and the most preferred selection object is the mechanical equipment with the shortest call waiting time.

[0055] It should be noted that the Monte Carlo method of the deduction algorithm is a numerical calculation method based on random sampling and statistical analysis, which is used to evaluate and predict various possible results and situations. By generating a large number of random samples and using statistical methods to analyze the samples, the behavior and results of the entire system can be inferred and predicted. It is applicable to situations where random factors and multiple variables need to be considered. In this embodiment, based on the matching degree to determine the result and the impact rate of the construction period, at this time, the impact rate of the construction period can be understood as the selection probability. Based on the impact rate of the construction period, the samples affecting this probability are determined, that is, the remaining order quantity of the mechanical equipment with qualified matching degree used in this implementation. According to the order quantity, the average usage duration is determined, and based on the average usage duration, the impact rate of the construction period of other construction sites that need to use this equipment is determined, that is, the determination of the impact rate in this embodiment is completed, and then the call waiting duration is determined.

[0056] Generate mechanical equipment that meets the demand orders based on the optimal strategy and perform supervised learning to obtain the final call method. In some possible implementation manners, perform supervised learning on the mechanical equipment with the shortest call waiting duration to obtain mechanical equipment with a call waiting time error lower than 10% and greater than or equal to 10%; perform call determination on the mechanical equipment with an error lower than 10%.

[0057] According to the mechanical equipment that meets the demand orders, perform classification based on the support vector machine to obtain a more appropriate call selection of the mechanical equipment. Specifically, when the mechanical equipment that meets the demand orders is determined, the support vector machine will use the previous call usage situation of this equipment as the test set to compare with the mechanical equipment that meets the demand orders described above to obtain the classification result. At this time, according to the classification result, the call result of the call equipment can be obtained more accurately. For example, when selecting the single-arm single-angle crane model 33390, the support vector machine in the remote system will perform a comparison based on the previous call situation of this model of mechanical equipment to obtain a classification that meets the preset result, that is, intuitively display the proportion of the waiting time of the equipment to be called. When the proportion of the waiting time error is greater than or equal to 10%, it is necessary to change the selected equipment model. On the contrary, when the error is less than 10%, it is not necessary. It should be noted that the error proportion here is the error proportion between the preset qualified call waiting time and the actual call waiting time. Thus, the final accurate call selection is ensured.

[0058] Embodiment 2

[0059] Based on the same concept as the method for calling construction machinery equipment based on big data in the foregoing embodiment, as Figure 2As shown, the present application also provides a call system for construction machinery and equipment based on big data. Exemplarily, the system can be divided into one or more modules, and one or more modules are stored in a memory and executed by a processor to complete the present application. One or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device. For example, the computer program can be divided into an external connection module, a call determination module, a decision-making module, and a result generation module. The specific functions of each module are as follows: The external connection module is used to connect to the information library of the target allocation transportation front end, obtain a demand order, and determine the longest call route based on the call type and call destination in the demand order; The call determination module, based on the target machinery and equipment library, performs determination of callable equipment and callable margin, and obtains the specific information of the callable equipment; Based on the longest route, determine the call result of the callable equipment; The decision-making module, based on the call result, performs matching decision-making, sorts the matching decision-making results, and obtains an optimal strategy; The result generation module generates construction machinery and equipment that meets the demand order based on the optimal strategy, and performs supervised learning to obtain the final call method.

[0060] In some possible implementation manners, the processor is data-connected to the external connection module, the call determination module, the decision-making module, and the result generation module.

[0061] The specific examples of the call method for construction machinery and equipment based on big data in the foregoing Embodiment 1 are equally applicable to the call system for construction machinery and equipment based on big data in this embodiment. Through the foregoing detailed description of the call method for construction machinery and equipment based on big data, those skilled in the art can clearly know the call system for construction machinery and equipment based on big data in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail herein.

[0062] The above shows and describes the basic principles, main features, and advantages of the present application. For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes falling within the meaning and scope of the equivalent elements of the claims in the present application. Any reference signs in the claims should not be regarded as limiting the claimed claims.

[0063] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment contains only one independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for calling engineering machinery equipment based on big data, the method being executed by a processor, characterized in that: The method comprises: Connecting to the information base of the target distribution transportation front end, obtaining the demand order, and determining the longest call route based on the call type and the call destination in the demand order, wherein the call type is the specified call equipment and the call destination is the specified call end point; Based on the target mechanical equipment library, determine the available equipment and available margin, and obtain the specific information of the available equipment; Determine the call result of the callable equipment based on the longest route; Based on the call results, perform matching decisions, sort the matching decision results, and obtain the optimal strategy; Based on the optimal strategy, mechanical equipment that meets the demand order is generated, and supervised learning is performed to obtain the final calling method.

2. The method according to claim 1, characterized in that The process of connecting to the information base of the target distribution transportation front end, obtaining the demand order, and determining the longest call route based on the call type and the call destination in the demand order includes: According to the demand order received in the information database, obtaining the demand details, including at least the order time, the order initiator and the order purpose, wherein the order purpose at least includes the call type and the call destination; Based on the call destination, identify and determine the longest call route with the same destination in the past.

3. The method according to claim 2, characterized in that The method of determining the callable equipment and the callable remainder based on the target mechanical equipment library to obtain specific information of the callable equipment includes at least: according to the mechanical equipment library corresponding to the address where the demand order is received, determining the type of equipment that can be called in the mechanical equipment library and the corresponding quantity of the callable equipment.

4. The method according to claim 3, characterized in that The determining of the call result of the callable equipment based on the longest route at least includes: According to the longest route information, determine the type of mechanical equipment that meets the route information; According to the mechanical equipment types that meet the longest route information, a sorting algorithm is used to obtain the quantity sorting of the corresponding mechanical equipment, a screening value is determined for the sorting result, and a screening sorting result with the quantity from most to least is obtained, and the equipment type corresponding to the screening sorting result is determined by the matching degree with the previous equipment type corresponding to the longest route; Based on the matching result and combined with the deduction algorithm, the impact rate of the remaining construction period after the mechanical equipment with qualified matching degree is called and the calling waiting time are determined to obtain the calling result.

5. The method according to claim 4, characterized in that Based on the call results, a matching decision is made, and the matching decision results are sorted to obtain the optimal strategy, which at least includes: The mechanical equipment that has an impact on the construction period will be re-evaluated, and the mechanical equipment that has no impact on the construction period will be called to wait for the duration to be determined; For mechanical equipment with a call waiting time greater than or equal to 1 day, execute the waiting action; for mechanical equipment with a call waiting time less than 1 day, execute the call waiting action; The call waiting objects are sorted to obtain the optimal selected object, where the optimal selected object is the mechanical equipment with the shortest call waiting time.

6. The method according to claim 5, characterized in that Generate mechanical equipment that meets the demand order based on the optimal strategy, and perform supervised learning to obtain the final calling method, which at least includes: Supervised learning is performed on the mechanical equipment with the shortest call waiting time to obtain mechanical equipment with a call waiting time error less than 10% and greater than or equal to 10%; call confirmation is performed on the mechanical equipment with an error less than 10%.

7. A calling system for engineering machinery equipment based on big data, the system comprising a processor, characterized in that: Also includes: An external connection module, the external connection module is used to connect to the information base of the target distribution transportation front end, obtain the demand order, and determine the longest call route based on the call type and call destination in the demand order; A calling determination module, wherein the calling determination module determines the callable equipment and the callable surplus based on the target mechanical equipment library to obtain specific information of the callable equipment; and determines the calling result of the callable equipment based on the longest route; A decision module, which performs matching decisions based on the call results, sorts the matching decision results, and obtains the optimal strategy; A result generation module generates mechanical equipment that meets the demand order based on an optimal strategy, and performs supervised learning to obtain a final calling method.

8. The system according to claim 7, characterized in that The processor is data-connected to the external connection module, the call determination module, the decision module, and the result generation module.

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