Resource scheduling system and method for wheel set maintenance workshop based on internet of things

Abstract

This application provides a resource scheduling system and method for wheelset maintenance workshops based on the Internet of Things (IoT), relating to the field of big data. It solves the technical problem that existing maintenance resource allocation strategies fail to effectively match the actual needs of wheelsets requiring maintenance, resulting in inefficient consumption of maintenance resources and operating costs. The method includes: acquiring data on wheelsets requiring maintenance in the maintenance workshop; performing preliminary screening on the wheelsets to obtain a first repair duration; determining whether the wheelsets need replacement based on the first repair duration; if yes, marking the wheelsets to be replaced as replacements; if no, constructing several constraints based on the next usage time, and constructing a multi-objective constraint function based on these constraints; allocating the wheelsets to be maintained based on the multi-objective constraint function and marking them as completed for resource scheduling in the wheelset maintenance workshop. This application is used in the resource scheduling process of wheelset maintenance workshops.

Description

Technical Field

[0001] This application relates to the field of big data, and in particular to a resource scheduling system and method for wheelset maintenance workshops based on the Internet of Things. Background Technology

[0002] Wheelsets are the basic running gear of railway vehicles. When wheelsets malfunction, they can be repaired using maintenance machines. However, if not repaired in time, they may cause serious consequences such as train derailment, excessive vibration, and accelerated wheel-rail wear. Traditional wheelset maintenance mainly relies on manual inspection and experience-based repair techniques, which suffer from low inspection accuracy, insufficient repair efficiency, and poor consistency in repair quality. With the development of railway transportation towards high speed and heavy load, the complexity of wheelset malfunctions and the professionalism of maintenance requirements have significantly increased, making it urgent to rely on intelligent maintenance machines to achieve precise and efficient repair.

[0003] In the maintenance of wheelsets awaiting repair, the existing maintenance equipment's resource allocation strategy fails to effectively match the actual needs of the wheelsets, resulting in maintenance equipment prioritizing wheelsets with long repair cycles and low usage priority, thus causing technical problems of inefficient consumption of maintenance resources and operation and maintenance costs. Summary of the Invention

[0004] This application provides a resource scheduling system and method for wheelset repair workshops based on the Internet of Things, which solves the technical problem that the allocation strategy of maintenance resources in existing maintenance equipment fails to effectively match the actual needs of wheelsets to be repaired, resulting in maintenance equipment prioritizing the service of wheelsets with long repair cycles and low priority of usage, thus causing inefficient consumption of maintenance resources and operation and maintenance costs.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, a resource scheduling method for wheelset maintenance workshops based on the Internet of Things is provided, including: acquiring data on wheelsets to be repaired in the maintenance workshop; wherein, the data on wheelsets to be repaired includes: the number of wheelsets to be repaired, the cause of the failure, and the next usage time; and performing preliminary screening on the wheelsets to be repaired to obtain the first repair time.

[0007] Retrieve the first repair time and determine whether the wheelset to be repaired needs to be replaced based on the first repair time; if so, mark the wheelset to be repaired as to be replaced with a new wheelset.

[0008] No, several constraints are constructed based on the next usage time, and a multi-objective constraint function is constructed based on these constraints; among them, the multi-objective constraint function includes: a function to maximize the utilization rate of maintenance equipment and a function to minimize the material waiting time;

[0009] Based on multi-objective constraint functions, wheelsets to be repaired are allocated and marked as resources for the workshop to complete wheelset repair.

[0010] Based on the above technical solution, in the resource scheduling method for wheelset repair workshop based on Internet of Things provided in this application, the first repair time of the wheelset to be repaired is quickly estimated based on historical maintenance data and current equipment status; if the first repair time exceeds the remaining usable value cycle of the wheelset, it is marked as a replacement with a new wheelset, thus avoiding the investment of resources in wheelsets with no repair value.

[0011] In conjunction with the first aspect above, in one possible implementation, the preliminary screening of the wheelset to be repaired to obtain the first repair time includes:

[0012] Obtain the fault severity of the i-th wheelset to be repaired and the historical repair time of similar wheelsets. The average historical repair time of the i-th wheelset to be repaired is used as the repair time benchmark. The severity of the fault includes: fault type Degree of damage and the complexity of the initial repair process ;

[0013] The severity of the fault is quantified and encoded using variables. Based on this quantified and encoded severity of the fault, the input dataset is constructed as follows: { , , , , The first repair time is obtained by least squares estimation.

[0014] In conjunction with the first aspect above, in one possible implementation, the quantification and encoding of the fault degree includes:

[0015] Retrieve all fault types, and use one-hot encoding to convert the j-th fault type after classification and coding into a numerical variable, denoted as . ; Obtain the fault type of the corresponding wheelset to be repaired, and record the corresponding fault type as 1; record the other types as 0;

[0016] The degree of damage is graded and assigned arithmetic progression values; the degree of damage includes: minor, moderate, and severe.

[0017] The quantitative indicators of the initial repair process complexity are retrieved and a quantitative indicator table is constructed. The initial repair process complexity is then assigned a value based on the quantitative indicator table. The quantitative indicators include: the number of key process steps, skill level requirements, professional equipment requirements, repair material grade, and spatial accessibility.

[0018] In conjunction with the first aspect above, in one possible implementation, obtaining the first repair time through the least squares estimation method includes:

[0019] Through formula The first repair time is calculated; where s is the sample size. The coefficient represents the fault type. A coefficient representing the degree of damage. This is a coefficient representing the initial complexity of the repair process. This is the error value; and ;

[0020] Minimize the sum of squared residuals Where p = 1, 2, 3;

[0021] The coefficients are obtained by minimizing the sum of squared residuals. Where k = 1, 2, 3;

[0022] Numeric variables Combined to form an n-row p+1-column matrix ,Will Combined to form an n x 1 matrix ;

[0023] Through formula = The error value was calculated. .

[0024] In conjunction with the first aspect above, in one possible implementation, determining whether the wheelset to be repaired should be replaced based on the first repair duration includes:

[0025] Retrieve the first repair time and the next usage time of the wheelset to be repaired; determine whether the first repair time is greater than the next usage time of the wheelset to be repaired; if yes, mark the corresponding wheelset to be repaired as to be replaced; otherwise, mark the corresponding wheelset to be repaired as not to be replaced.

[0026] In conjunction with the first aspect above, in one possible implementation, the construction of several constraints based on the next usage time includes:

[0027] The constraints include: constraint one, constraint two and constraint three;

[0028] Among them, the first condition is: ; For device i, process the variables for the kth faulty wheel pair;

[0029] Condition 2 is set as follows In the formula, Let j be the quantity of material j consumed in the k-th round. This refers to the current inventory of the materials. Let k be the amount of material in transit; k = 1, 2, 3, ..., D;

[0030] Condition three is set as follows: In the formula, For material j, the actual usage time. Let be the arrival time of material j.

[0031] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the function for maximizing the utilization rate of the maintenance equipment includes:

[0032] Through formula Construct a function to maximize equipment utilization In the formula, Let i be the utilization rate of the i-th maintenance equipment; The planned working time after the wheelset to be repaired is completed. This is the next time the wheelset awaiting maintenance will be used.

[0033] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the material waiting time minimization function includes:

[0034] Through formula Construct a function to minimize material waiting time In the formula, Let be the waiting time for the j-th material.

[0035] Secondly, it provides a resource scheduling system for wheelset maintenance workshops based on the Internet of Things, including: a communication unit and a processing unit;

[0036] The communication unit is used to acquire wheelset data to be repaired in the maintenance workshop; wherein, the wheelset data to be repaired includes: the number of wheelsets to be repaired, the cause of the failure and the next usage time; and to perform preliminary screening of the wheelsets to be repaired to obtain the first repair time;

[0037] The processing unit is used to retrieve the first repair time and determine whether the wheelset to be repaired needs to be replaced based on the first repair time; if so, the wheelset to be repaired is marked as to be replaced with a new wheelset.

[0038] No, construct a multi-objective constraint function based on the next usage time; the multi-objective constraint function includes: a function to maximize the utilization rate of maintenance equipment and a function to minimize the material waiting time;

[0039] Based on multi-objective constraint functions, wheelsets to be repaired are allocated and marked as resources for the workshop to complete wheelset repair.

[0040] In conjunction with the second aspect above, in one possible implementation, the system further includes: a feedback scheduling unit: used to determine whether it is necessary to re-input several constraints based on the execution effect until the target function is reached, and then mark it as completed.

[0041] This application provides a resource scheduling method and system for wheelset maintenance workshops based on the Internet of Things (IoT). Through mathematical modeling, it maximizes the proportion of actual equipment working time while meeting time constraints; minimizes material waiting time by coordinating maintenance task start times with material arrival times to reduce material waiting time in inventory; allocates maintenance resources to "high-priority, short-cycle, high-value" wheelsets using real-time data and constraints; and reduces operation and maintenance costs by balancing equipment utilization and material waiting time through multi-objective functions.

[0042] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0043] Figure 1 A system architecture diagram of a resource scheduling system for a wheelset maintenance workshop based on the Internet of Things provided in this application embodiment;

[0044] Figure 2 A flowchart illustrating the resource scheduling method for a wheelset maintenance workshop based on the Internet of Things provided in this application embodiment;

[0045] Figure 3 A flowchart illustrating another IoT-based resource scheduling method for wheelset maintenance workshops provided in this application embodiment; Detailed Implementation

[0046] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0047] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0048] The resource scheduling system for wheelset maintenance workshop based on the Internet of Things provided in this application embodiment includes a communication unit and a processing unit.

[0049] The communication unit is used to acquire wheelset data to be repaired in the maintenance workshop; wherein, the wheelset data to be repaired includes: the number of wheelsets to be repaired, the cause of the failure and the next usage time; and to perform preliminary screening of the wheelsets to be repaired to obtain the first repair time;

[0050] The processing unit is used to retrieve the first repair time and determine whether the wheelset to be repaired needs to be replaced based on the first repair time; if so, the wheelset to be repaired is marked as to be replaced with a new wheelset.

[0051] No, construct a multi-objective constraint function based on the next usage time; the multi-objective constraint function includes: a function to maximize the utilization rate of maintenance equipment and a function to minimize the material waiting time;

[0052] Based on multi-objective constraint functions, wheelsets to be repaired are allocated and marked as resources for the workshop to complete wheelset repair.

[0053] To address the technical problem in existing technologies where the allocation strategy for maintenance resources in maintenance equipment fails to effectively match the actual needs of wheelsets requiring maintenance, resulting in inefficient consumption of maintenance resources and operating costs, this application provides a resource scheduling method for wheelset maintenance workshops based on the Internet of Things (IoT). Figure 1 As shown, the method includes: acquiring wheelset data to be repaired in the repair shop; wherein, the wheelset data to be repaired includes: the number of wheelsets to be repaired, the cause of the failure, and the next usage time; and performing preliminary screening on the wheelsets to be repaired to obtain the first repair time;

[0054] Retrieve the first repair time and determine whether the wheelset to be repaired needs to be replaced based on the first repair time; if so, mark the wheelset to be repaired as to be replaced with a new wheelset.

[0055] No, several constraints are constructed based on the next usage time, and a multi-objective constraint function is constructed based on these constraints; among them, the multi-objective constraint function includes: a function to maximize the utilization rate of maintenance equipment and a function to minimize the material waiting time;

[0056] Based on multi-objective constraint functions, wheelsets to be repaired are allocated and marked as completed for resource scheduling in the workshop. Based on this, the deadline for repair tasks is set according to the next usage time of the wheelsets to avoid task delays. Combined with material inventory and procurement cycle, the start time of repair tasks is constrained.

[0057] like Figure 2 As shown in the embodiments of this application, the resource scheduling method for wheelset maintenance workshops based on the Internet of Things includes:

[0058] The wheelset to be repaired is initially screened to obtain the first repair time.

[0059] In some implementations, the degree of failure of the i-th wheelset to be repaired and the historical repair time of similar wheelsets are obtained. The average historical repair time of the i-th wheelset to be repaired is used as the repair time benchmark. The severity of the fault includes: fault type Degree of damage and the complexity of the initial repair process ;

[0060] Retrieve all fault types, and use one-hot encoding to convert the j-th fault type after classification and coding into a numerical variable, denoted as . ; Obtain the fault type of the corresponding wheelset to be repaired, and record the corresponding fault type as 1; record the other types as 0;

[0061] The degree of damage is graded and assigned arithmetic progression values; the degree of damage includes: minor, moderate, and severe.

[0062] The quantitative indicators of the initial repair process complexity are retrieved and a quantitative indicator table is constructed. The initial repair process complexity is then assigned a value based on the quantitative indicator table. The quantitative indicators include: the number of key process steps, skill level requirements, professional equipment requirements, repair material grade, and spatial accessibility.

[0063] The input dataset is constructed based on the fault severity after variable quantization and encoding: { , , , ,}, through formula The first repair time is calculated; where s is the sample size. The coefficient represents the fault type. A coefficient representing the degree of damage. This is a coefficient representing the initial complexity of the repair process. This is the error value; and ;

[0064] Minimize the sum of squared residuals Where p = 1, 2, 3;

[0065] The coefficients are obtained by minimizing the sum of squared residuals. Where k = 1, 2, 3;

[0066] Numeric variables Combined to form an n-row p+1-column matrix ,Will Combined to form an n x 1 matrix ;

[0067] Through formula = The error value was calculated. .

[0068] For example, step 1: Fault severity quantification and variable encoding:

[0069] The severity of a fault includes three dimensions: fault type, damage level, and complexity of initial repair process. These dimensions need to be converted into numerical variables that the model can handle.

[0070] 1.1 Fault Type: One-Heat Code:

[0071] Assume there are 3 types of historical faults in this depot (p=3): Type 1: Wheel tread peeling (the fault type of the wheelset currently under repair); Type 2: Axle cracks; Type 3: Bearing wear;

[0072] One-hot encoding is used to convert categorical variables into numerical variables: each fault type corresponds to a binary variable (0 or 1) that indicates "whether it belongs to this type".

[0073] The fault type of the wheelset to be repaired is "wheel tread peeling", so the coding results are: x11=1 (belongs to type 1), x12=0 (does not belong to type 2), x13=0 (does not belong to type 3).

[0074] 1.2 Damage severity: Graded arithmetic progression:

[0075] The degree of damage is divided into "minor, moderate, and severe", and an arithmetic progression method is used (with a tolerance of 1): minor: 1 point; moderate: 2 points; severe: 3 points;

[0076] The damage level of the wheelset currently under repair is assessed as "moderate", therefore, the value is assigned as: x14=2 (Note: the variable number here can be adjusted according to the actual model; assuming the damage level is the 4th variable).

[0077] 1.3 Preliminary Repair Process Complexity: Construction and Value Assignment of Quantitative Index Table (as shown in Table 1);

[0078] Process complexity includes 5 quantitative indicators, and a scoring standard (1-5 points, with higher scores indicating greater complexity) needs to be set for each indicator:

[0079] Table 1 Quantitative Indicators

[0080]

[0081] like Figure 3 As shown in the embodiment of this application, another resource scheduling method for wheelset maintenance workshop based on the Internet of Things includes: constructing several constraints based on the next use time, and constructing a multi-objective constraint function based on the several constraints.

[0082] The multi-objective constraint functions include: the function for maximizing the utilization rate of maintenance equipment and the function for minimizing material waiting time.

[0083] In some implementations, several constraints include: constraint one, constraint two, and constraint three;

[0084] Among them, the first condition is: ; For device i, process the variables for the kth faulty wheel pair;

[0085] Condition 2 is set as follows In the formula, Let j be the quantity of material j consumed in the k-th round. This refers to the current inventory of the materials. Let k be the amount of material in transit; k = 1, 2, 3, ..., D;

[0086] Condition three is set as follows: In the formula, For material j, the actual usage time. Let J be the arrival time of material j;

[0087] Through formula Construct a function to maximize equipment utilization In the formula, Let i be the utilization rate of the i-th maintenance equipment; The planned working time after the wheelset to be repaired is completed. The next time the wheelset awaiting repair will be used;

[0088] Through formula Construct a function to minimize material waiting time In the formula, Let be the waiting time for the j-th material.

[0089] It should be noted that, if the i-th maintenance equipment only processes the k-th wheelset to be maintained, =1; otherwise =0.

[0090] For example, a railway vehicle depot's maintenance workshop needs to operate on an 8-hour workday (total time window T). 总 Complete the repair of 3 wheelsets (wheelset 1, wheelset 2, and wheelset 3) within 8 hours;

[0091] Given: Repair equipment: Equipment 1 (CNC wheel lathe), Equipment 2 (automatic flaw detector);

[0092] Wheelset to be repaired: Wheelset 1 (Fault type: Wheel tread peeling, repair time base = 10 hours).

[0093] Wheelset 2 (Fault type: axle crack, repair time base = 8 hours);

[0094] Wheelset 3 (Fault type: bearing wear, repair time base = 12 hours);

[0095] Key materials: bearings (material 1), lubricating grease (material 2);

[0096] Material inventory and in-transit quantity: Bearing inventory S1=3 units, in-transit quantity Q1=1 unit (expected to arrive in 2 hours); Lubricating grease inventory S2=2kg, in-transit quantity Q2=1kg (expected to arrive in 1 hour).

[0097] Step 1: Define variables and constraints

[0098] 1.1 Equipment Assignment Variables (Constraint 1)

[0099] Let x ik Let i be a 0-1 variable (i=1,2; k=1,2,3) indicating whether device i processes wheel pair k:

[0100] x ik =1: Device i processes wheel pair k; x ik =0: Device i does not process wheel pair k.

[0101] Constraint logic: Each device can only process one wheelset at a time (device capacity constraint);

[0102] Each wheelset is processed by only one device (task uniqueness constraint);

[0103] 1.2 Material supply and demand constraints (Constraint 2)

[0104] Let a jk Given "the quantity of material j consumed by wheel pair k" (j=1, 2; k=1, 2, 3), the total consumption of material j must be ≤ inventory + in-transit quantity: k=1;

[0105] Example parameters: Wheelset 1 consumes 2 bearings (a) 11 =2), 0.5kg of lubricating grease (a 21 =0.5);

[0106] Wheelset 2 consumes 1 bearing (a) 12 =1), Lubricating grease 0kg (a 22 =0);

[0107] Wheelset 3 consumes 0 bearings (a) 13 =0), 1kg of lubricating grease (a 23 =1);

[0108] Total bearing requirement: 2x11 + 1x21 + 0x31 ≤ 3 + 1 = 4;

[0109] Total grease requirement: 0.5x11 + 0x21 + 1x31 ≤ 2 + 1 = 3.

[0110] 1.3 Material Time Constraints (Constraint Condition Three)

[0111] Let t use，jk Let t be the "actual usage time of material j on wheelset k". arrive，j Let t be the "delivery time of material j". use，jk ≥t arrive，j j, k;

[0112] Example parameter: bearing delivery time t arrive，1 =2 hours (Current time = 0 hours, delivery will be in 2 hours);

[0113] Grease delivery time t arrive，2 =1 hour (Current time = 0 hours, delivery will be in 1 hour);

[0114] If wheelset 1 is processed by device 1, the start time is t. start，1 Then its completion time t end，1 =t start，1 +10 hours, usage time of material 1 tuse，11 =t end，1 (Assuming assembly is performed immediately after repair), t must be satisfied. use，11 ≥2 hours.

[0115] Based on the above technical solution, by setting constraints, the same maintenance equipment can only process one wheelset at a time, and the amount of material used cannot exceed the material inventory and the amount of material in transit. At the same time, the start time of equipment i in processing wheelset k to be repaired is later than its start time and the arrival time of material j, so that the data scheduling in the wheelset maintenance workshop is more reasonable and the utilization efficiency of maintenance equipment is increased.

[0116] The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as a resource scheduling system for a wheelset maintenance workshop based on the Internet of Things, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0118] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0119] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A resource scheduling method for a wheelset maintenance workshop based on the Internet of Things, characterized in that, include: Obtain data on wheelsets awaiting repair in the maintenance workshop; this data includes the number of wheelsets awaiting repair, the cause of the malfunction, and the next usage time; perform preliminary screening of the wheelsets awaiting repair to obtain the initial repair time; Retrieve the first repair time and determine whether the wheelset to be repaired needs to be replaced based on the first repair time; if so, mark the wheelset to be repaired as to be replaced with a new wheelset. No, several constraints are constructed based on the next usage time, and a multi-objective constraint function is constructed based on these constraints; among them, the multi-objective constraint function includes: a function to maximize the utilization rate of maintenance equipment and a function to minimize the material waiting time; Based on multi-objective constraint functions, wheelsets to be repaired are allocated and marked as resources for the workshop where wheelset repairs are to be completed; among them, The step of determining whether the wheelset to be repaired should be replaced based on the first repair time includes: Retrieve the first repair time and the next usage time of the wheelset to be repaired; determine whether the first repair time is greater than the next usage time of the wheelset to be repaired; if yes, mark the corresponding wheelset to be repaired as to be replaced; otherwise, mark the corresponding wheelset to be repaired as not to be replaced. The method for obtaining the function for maximizing the utilization rate of the maintenance equipment includes: Through formula Construct a function to maximize equipment utilization In the formula, Let i be the utilization rate of the i-th maintenance equipment; The planned working time after the wheelset to be repaired is completed. The next time the wheelset awaiting repair will be used; The method for obtaining the material waiting time minimization function includes: By the formula Constructing a material waiting time minimization function ; where, is the jth material waiting time; is the actual usage time of material j, is the arrival time of material j.

2. The method for resource scheduling of an Internet of Things based wheelset maintenance workshop according to claim 1, characterized in that, The preliminary screening of the wheelset to be repaired to obtain the first repair time includes: Obtain the fault severity of the i-th wheelset to be repaired and the historical repair time of similar wheelsets. The average historical repair time of the i-th wheelset to be repaired is used as the repair time benchmark. The severity of the fault includes: fault type Degree of damage and the complexity of the initial repair process ; The severity of the fault is quantified and encoded using variables. Based on this quantified and encoded severity of the fault, the input dataset is constructed as follows: { , , , , The first repair time is obtained by least squares estimation.

3. The method for resource scheduling of an Internet of Things based wheelset maintenance workshop according to claim 2, characterized in that, The process of quantifying and encoding the degree of failure includes: Retrieve all fault types, and use one-hot encoding to convert the j-th fault type after classification and coding into a numerical variable, denoted as . ; Obtain the fault type of the corresponding wheelset to be repaired, and record the corresponding fault type as 1; record the other types as 0; The degree of damage is graded and assigned arithmetic progression values; the degree of damage includes: minor, moderate, and severe. The quantitative indicators of the initial repair process complexity are retrieved and a quantitative indicator table is constructed. The initial repair process complexity is then assigned a value based on the quantitative indicator table. The quantitative indicators include: the number of key process steps, skill level requirements, professional equipment requirements, repair material grade, and spatial accessibility.

4. The method for resource scheduling of an Internet of Things based wheelset maintenance workshop according to claim 2, characterized in that, The process of obtaining the first repair time using the least squares estimation method includes: Through formula The first repair time is calculated; where s is the sample size. The coefficient represents the fault type. A coefficient representing the degree of damage. This is a coefficient representing the initial complexity of the repair process. This is the error value; and ; Minimizing the residual sum of squares where p = 1, 2, 3; Obtaining coefficients by minimizing a residual sum of squares ; where k = 1, 2, 3; numerical type variable integrated to form an n by p+1 matrix , the integrated to form an n by 1 matrix ; The error value is calculated by the formula = ​ 5. The method for resource scheduling of an internet of things based wheelset maintenance workshop according to claim 1, characterized in that, The constraints constructed based on the next usage time include: The constraints include: constraint one, constraint two and constraint three; wherein the first condition is: ; is a variable for device i to handle the kth faulty wheel set; The second condition is set ; where, is the amount of material j consumed by the kth wheel pair, is the current inventory of material j, is the amount of material j in transit; k = 1, 2, 3,..., D; The third condition is set as: wherein, is the actual usage time of material j, is the arrival time of material j.

6. A resource scheduling system for wheelset maintenance workshops based on the Internet of Things, characterized in that, include: Communication unit and processing unit; The communication unit is used to acquire wheelset data to be repaired in the maintenance workshop; wherein, the wheelset data to be repaired includes: the number of wheelsets to be repaired, the cause of the failure and the next usage time; and to perform preliminary screening of the wheelsets to be repaired to obtain the first repair time; The processing unit is used to retrieve the first repair time and determine whether the wheelset to be repaired needs to be replaced based on the first repair time; if so, the wheelset to be repaired is marked as to be replaced with a new wheelset. No, construct a multi-objective constraint function based on the next usage time; the multi-objective constraint function includes: a function to maximize the utilization rate of maintenance equipment and a function to minimize the material waiting time; Based on multi-objective constraint functions, wheelsets to be repaired are allocated and marked as resources for the workshop where wheelset repairs are to be completed; among them, The step of determining whether the wheelset to be repaired should be replaced based on the first repair time includes: Retrieve the first repair time and the next usage time of the wheelset to be repaired; determine whether the first repair time is greater than the next usage time of the wheelset to be repaired; if yes, mark the corresponding wheelset to be repaired as to be replaced; otherwise, mark the corresponding wheelset to be repaired as not to be replaced. The method for obtaining the function for maximizing the utilization rate of the maintenance equipment includes: Through formula Construct a function to maximize equipment utilization In the formula, Let i be the utilization rate of the i-th maintenance equipment; The planned working time after the wheelset to be repaired is completed. The next time the wheelset awaiting repair will be used; The method for obtaining the material waiting time minimization function includes: By the formula Constructing a material waiting time minimization function ; where, is the jth material waiting time; is the actual usage time of material j, is the arrival time of material j.

7. The resource scheduling system for wheelset maintenance workshops based on Internet of Things according to claim 6, characterized in that, Also includes: Feedback scheduling unit: Used to determine whether several constraints need to be re-entered based on the execution effect, until the objective function is reached and marked as completed.

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