Vehicle maintenance worker evaluation method and vehicle maintenance worker evaluation device

Abstract

To accurately determine an evaluation index of a worker.SOLUTION: A vehicle maintenance worker evaluation device comprises a repair / maintenance content database 4 and a worker evaluation calculation unit 6. The repair / maintenance content database 4 contains a vehicle ID of a vehicle, date and time of repair or maintenance, a worker ID of the maintenance worker who carried out the repair or maintenance, the details of the repair or maintenance, and the history of any additional maintenance carried out on the same part of the same vehicle after the repair or maintenance. The worker evaluation calculation unit 6 references the repair / maintenance content database 4 to determine the repair performance 7b, which serves as an evaluation index about a specific worker.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method and an apparatus for evaluating vehicle maintenance workers.

Background Art

[0002] Recently, in order to solve the shortage of workers who perform maintenance work including repair and replacement of vehicle parts of vehicles, there has been a tendency to mobilize workers with maintenance work qualifications from other industries than the automotive industry to the automotive industry. When this mobilization occurs, it is conceivable that the worker goes to a dealer or a repair shop, or goes on a business trip to an individual or a corporation.

[0003] Further, Patent Document 1 discloses an evaluation method for evaluating the skills of vehicle workers based on the vehicle state as vehicle failure information and the working time etc. as maintenance work information performed on the vehicle failure.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Generally, after maintenance work is performed on a failure at one location of a vehicle, additional maintenance work may be performed on the same location of the same vehicle. For example, if the previously performed maintenance work is incorrect work, the failure recurs in the vehicle, and thus the vehicle user is requested to restock, and the worker performs additional maintenance work on the vehicle.

[0006] However, in the evaluation method of Patent Document 1, the maintenance work information does not include the history of additional maintenance work performed on the same part of the same vehicle after one maintenance work. Therefore, in the evaluation method of Patent Document 1, even if a correct maintenance work is performed to eliminate an error after an incorrect maintenance work, they are simply evaluated without associating the two, so the evaluation index of the operator cannot be accurately obtained.

Means for Solving the Problems

[0007] In the evaluation method of a vehicle maintenance worker of the present invention, a repair and maintenance content database including a vehicle ID of a vehicle, a date and time when maintenance work was performed, an operator ID, the content of the maintenance work, and a history of additional maintenance work performed on the same part of the same vehicle after this maintenance work is created. Then, with reference to this repair and maintenance content database, an evaluation index for a specific operator is obtained.

Effects of the Invention

[0008] According to the present invention, by clearly defining the relevance between an incorrect maintenance work and a correct additional maintenance work and justifiably evaluating the additional maintenance work, the evaluation index of the operator can be accurately obtained.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

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Figure 4

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Figure 6

Figure 7

Figure 8

Mode for Carrying Out the Invention

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] FIG. 1 is a functional block diagram of an evaluation device for vehicle maintenance workers in the first embodiment. In FIG. 1, in addition to the functional block diagram of the evaluation device for vehicle maintenance workers, repair and maintenance content data 4a stored in the repair and maintenance content database 4 and worker evaluation 7a stored in the worker database 7 and displayed on the terminal 8 are shown.

[0012] The evaluation device for vehicle maintenance workers includes a communication unit 1, a store / worker / date and time input / output unit 2, a failure diagnosis result database 3, a repair and maintenance content database (repair and maintenance content storage unit) 4, a contribution degree / difficulty calculation unit 5, a worker evaluation calculation unit 6, and a worker database 7.

[0013] The communication unit 1 is configured to communicate work information between the terminal 8 used by the user of the vehicle receiving the maintenance work and the evaluation device for vehicle maintenance workers. When the user is an individual, the terminal 8 is a mobile terminal owned by the user, such as a smartphone. When the user is a person belonging to a company, the terminal 8 is a terminal owned by the company, such as a personal computer (PC).

[0014] The store / operator / date and time input / output unit 2 inputs information regarding maintenance work performed by the operator (hereinafter referred to as "maintenance work information") and outputs this information to the failure diagnosis result database 3 and the repair / overhaul content database 4. In this embodiment, the maintenance work indicates any one of repair, overhaul, and replacement among the work performed on the failure of vehicle parts. In this embodiment, for the sake of brevity, the expression "repair / overhaul" is appropriately used as an expression corresponding to the maintenance work. In this embodiment, the maintenance work information includes the vehicle ID of the vehicle targeted for the maintenance work, the date and time when the maintenance work was performed (repair / overhaul date and time), the operator ID of the operator who performed the maintenance work, the content of the maintenance work (repair / overhaul content), the working time of the maintenance work, the result after the maintenance work (result after repair / overhaul), the location where the maintenance work was performed (store, etc.), and the failure diagnosis result diagnosed by the failure diagnosis machine. The vehicle ID of the vehicle, the repair / overhaul date and time, the operator ID, the content of the maintenance work, the working time, the result after the maintenance work, and the location of the maintenance work are recorded in the repair / overhaul content database 4. Also, the failure diagnosis result is recorded in the failure diagnosis result database 3.

[0015] Also, the operator engaged in the maintenance work connects a failure diagnosis machine well-known to the computer system installed in the automobile to recognize the failure diagnosis result, that is, the failure code indicating the failure of the vehicle parts and its cause, and performs the maintenance work on this failure code.

[0016] The failure diagnosis result database 3 stores failure codes, which are the failure diagnosis results for various components of different vehicles. Also, as one form of data, the failure diagnosis result database 3 has data divided by the type of failure code, which is the failure diagnosis result, that is, data divided by the type of diagnosis result (All type D data). The data divided by the type of diagnosis result (All type D data) includes the number of occurrences or frequency and the number of additional maintenance for each type of diagnosis result. Furthermore, as one form of data, the failure diagnosis result database 3 has data narrowed down to the same diagnosis result as the evaluation target. The data narrowed down to the same diagnosis result as the evaluation target includes the number of occurrences or frequency of the same diagnosis result as the evaluation target. For example, when the failure code indicating the failure diagnosis result among the information input by the operator shows an engine misfire due to an injector failure, the failure diagnosis result database 3 has data narrowed down to the engine misfire due to an injector failure. Also, as other causes of engine misfire, for example, abnormal ignition due to a plug defect can be cited.

[0017] The repair and maintenance content database 4 is created by accumulating data related to the vehicle ID of the vehicle, the operator ID of the vehicle, the repair and maintenance content, the repair and maintenance date and time, the working hours required for the repair and maintenance content, the repair and maintenance costs required for the repair and maintenance content, the contribution degree described below calculated by the contribution degree and difficulty calculation unit 5, the difficulty described below also calculated by the contribution degree and difficulty calculation unit 5, the result after repair and maintenance, the number of parts replaced during the maintenance work, and the correct rate of the repair and maintenance content. This data is accumulated for all of various vehicles. Further, the repair and maintenance content database 4 has, as one form of data, data (All type X data) divided for each repair and maintenance. The data (All type X data) divided for each repair and maintenance includes the repair and maintenance costs for each repair and maintenance content. Furthermore, the repair and maintenance content database 4 has, as one form of data, data narrowed down to the same repair and maintenance content as the evaluation target. The data narrowed down to the same repair and maintenance content as the evaluation target includes the repair and maintenance costs and working hours of the same repair and maintenance content as the evaluation target. For example, when the repair and maintenance content among the information input by the operator is the replacement of the injector, the repair and maintenance content database 4 has data narrowed down to the replacement of the injector.

[0018] Also, the "result after repair and maintenance" means that when an operator performs a specific maintenance work on one part of a specific vehicle and the function or state of the part is good, it becomes "good", while when the function or state of the part is bad and re-stocking for additional maintenance work is required, it becomes "re-stocking". When re-stocking is required in this way, after the maintenance work performed on one part of a specific vehicle, additional maintenance work is performed on the same part of the same vehicle. Therefore, it can be said that the repair and maintenance content database 4 includes the history of the additional maintenance work performed on the same part of the same vehicle after the previous maintenance work.

[0019] In addition, the "correct rate" refers to the ratio of the number of repair and maintenance tasks with "good" results among the same multiple repair and maintenance tasks performed by multiple workers. For example, when the replacement of the EV battery module is carried out 10 times by multiple workers and all the results after repair and maintenance are judged to be "good", assuming only "good" exists as the result after repair and maintenance, the correct rate is 100%.

[0020] As shown in FIG. 1, in the repair and maintenance content data 4a of the repair and maintenance content database 4, for example, a worker with a worker ID indicated by "A" performs the replacement of the EV battery module on a specific vehicle (vehicle ID) at a specific store as the work location for 3 hours at 15:01 on April 1, 2019 (shown as "2019 / 4 / 1 15:01" in FIG. 1), and records as maintenance work information that the result after repair and maintenance was good. Note that in FIG. 1, the descriptions of the work location, vehicle ID, and correct rate are omitted. Here, although the item of the repair and maintenance content in FIG. 1 is shown as "EV battery module replacement", the actual repair and maintenance content indicates a series of operations from starting to connect a well-known fault diagnostic machine to the computer system installed in the automobile until recognizing the fault code related to the EV battery module and completing the replacement of the EV battery module for this fault code. Further, when replacing this EV battery module, the number of actually replaced parts is grasped and included in the repair and maintenance content database 4.

[0021] The contribution degree and difficulty calculation unit 5 calculates the contribution degree and difficulty by referring to the fault diagnosis result stored in the fault diagnosis result database 3 and the repair and maintenance content stored in the repair and maintenance content database 4. Here, the "contribution degree" indicates the benefit brought to the vehicle user by the maintenance work when the worker performs the maintenance work. Also, the "difficulty" indicates the difficulty of the maintenance work when the worker performs the maintenance work. The contribution degree and difficulty are updated each time they are calculated along with the repair and maintenance by the worker and are included in the repair and maintenance content database 4.

[0022] Also, after the maintenance work of the vehicle, if there is a re-stocking of the vehicle for additional maintenance work at the same location of the same vehicle, the contribution degree for the corresponding maintenance work in the repair and maintenance content database 4 is corrected to be low. The reason for making such a correction is that it is presumed that the previously performed maintenance work was incorrect and the additional maintenance work occurred to compensate for this incorrect maintenance work. Therefore, the contribution degree should be appropriately corrected to be low, taking into account the low contribution degree of the incorrect maintenance work.

[0023] Next, while referring to FIG. 2, as one process performed by the contribution degree and difficulty calculation unit 5, the method for calculating the contribution degree will be described.

[0024] First, in step S100, as the maintenance work information performed by the operator this time, the result R after repair and maintenance, the fault diagnosis result D, the repair and maintenance content X, the occurrence frequency N of the fault diagnosis result, the working time T, and the repair and maintenance cost C are registered.

[0025] Next, in step S101, the repair and maintenance content database 4 and the fault diagnosis result database 3 are referred to.

[0026] Then, in step S102, data with good results after repair and maintenance is extracted from the repair and maintenance content database 4.

[0027] Next, in step S103, as past data, data (All type D data) divided by diagnosis result type is extracted from the fault diagnosis result database 3. That is, data divided by fault code for all locations or parts of the vehicle is extracted.

[0028] After extracting the data classified by diagnosis result type, the process proceeds to step S104, where the average value Avg.(Ttl.N(D_i)) of the total number or frequency of occurrences for each diagnosis result type is calculated. This average value Avg.(Ttl.N(D_i)) serves as the contribution calculation reference value for calculating the contribution degree.

[0029] Also, in parallel with the extraction of the data classified by diagnosis result type in step S103, in step S105, as past data, data narrowed down to the same diagnosis result as the evaluation target is extracted. For example, when the operator performs repair and maintenance on engine misfires due to injector failures as the current operation, data narrowed down to engine misfires due to injector failures is extracted.

[0030] Then, in step S106, the total number or frequency of occurrences Ttl.N(D_1) of the same diagnosis result as the evaluation target is calculated. That is, the total number or frequency of occurrences of past maintenance operations identical to the maintenance operation performed by the operator this time is obtained.

[0031] Next, in step S107, it is determined whether the total Ttl.N(D_1) is lower than the average value Avg.(Ttl.N(D_i)). If the total Ttl.N(D_1) is lower than the average value Avg.(Ttl.N(D_i)), in step S108, the contribution degree is increased.

[0032] Also, if the total Ttl.N(D_1) is equal to or higher than the average value Avg.(Ttl.N(D_i)) in step S107, the process proceeds to step S109 to decrease the contribution degree.

[0033] Also, in another step S110 following step S102, data (All type X data) classified by repair and maintenance content is extracted from the repair and maintenance content database 4 as past data. That is, data classified by the repair and maintenance content implemented for all locations or parts of the vehicle is extracted.

[0034] After extracting data divided according to repair and maintenance contents, in step S111, the average value Avg.C(X) of the repair and maintenance costs for each repair and maintenance content is calculated. This average value Avg.C(X) serves as a contribution calculation reference value for calculating the contribution degree.

[0035] Also, in parallel with the extraction of data divided according to the types of diagnosis results in step S110, in step S112, as past data, data narrowed down to the same repair and maintenance content as the evaluation target is extracted. For example, when an operator performs an injector replacement as the current operation, data narrowed down to the injector replacement is extracted.

[0036] After extracting data narrowed down to the same repair and maintenance content as the evaluation target, in step S113, the average value Avg.C(X_1) of the repair and maintenance costs for the same repair and maintenance content as the evaluation target is calculated. That is, the average value of the past repair and maintenance costs of the same maintenance work as the maintenance work performed by the operator this time is obtained. This average value Avg.C(X_1) is also used as a contribution calculation reference value for calculating the contribution degree in step S117 described later.

[0037] Next, in step S114, it is determined whether the average value Avg.C(X_1) is greater than the average value Avg.C(X). If the average value Avg.C(X_1) is greater than the average value Avg.C(X), in step S115, the contribution degree is increased.

[0038] Also, if it is determined in step S114 that the average value Avg.C(X_1) is less than or equal to the average value Avg.C(X), the process proceeds to step S116, and the contribution degree is decreased.

[0039] Also, in another step S117 following step S113, it is determined whether the repair and maintenance cost C required for the work performed by the operator this time is greater than the average value Avg.C(X_1). If the repair and maintenance cost C is greater than the average value Avg.C(X_1), in step S118, the contribution degree is increased.

[0040] Also, when it is determined in step S117 that the repair and maintenance cost C is less than or equal to the average value Avg.C(X_1), the process proceeds to step S119 to reduce the contribution degree.

[0041] Also, in another step S120 following step S112, the average value Avg.T(X_1) of the working hours for the repair and maintenance content same as that of the evaluation target is calculated. That is, the average working hours required for the same maintenance work as the maintenance work performed by the operator this time are obtained. This average value Avg.T(X_1) serves as the contribution degree calculation reference value for calculating the contribution degree.

[0042] Next, in step S121, it is determined whether the actual working hours T required for the work performed by the operator this time are shorter than the average value Avg.T(X_1). If the working hours T are shorter than the average value Avg.T(X_1), in step S122, the contribution degree is increased. More specifically, the shorter the actual working hours T are compared to the average value Avg.T(X_1), that is, the average working hours required for the same maintenance work, the higher the contribution degree of the maintenance work is considered.

[0043] Also, when it is determined in step S121 that the working hours T are greater than or equal to the average value Avg.T(X_1), the process proceeds to step S123 to reduce the contribution degree.

[0044] Also, although not shown in the flowchart of FIG. 2, by referring to the repair and maintenance content database 4, the average number of parts replaced during the same maintenance work is obtained, and when the actual number of parts replaced is more than this average number of parts, it may be determined that the contribution degree of the maintenance work is low.

[0045] In Fig. 2, the method for calculating the contribution degree using the references of the failure diagnosis result database 3 and the repair and maintenance content database 4 has been described. However, the contribution degree may also be calculated by other calculation methods, for example, a calculation method using machine learning. Further, by weighting the parameters for calculating the contribution degree, the contribution degree associated with more important parameters can be increased.

[0046] Next, while referring to Fig. 3, as one process implemented in the contribution degree and difficulty calculation unit 5, the method for calculating the difficulty will be described.

[0047] First, in step S200, as the work content performed by the operator this time, the result R after repair and maintenance, the failure diagnosis result D, the repair and maintenance content X, the occurrence frequency N of the failure diagnosis result, the working time T, and the repair and maintenance cost C are registered.

[0048] Next, in step S201, the repair and maintenance content database 4 and the failure diagnosis result database 3 are referred to.

[0049] Then, in step S202, from the failure diagnosis result database 3, as past data, data (All type D data) divided by diagnosis result type is extracted. That is, data divided by failure code for all locations or parts of the vehicle is extracted.

[0050] After the extraction of data divided by diagnosis result type, the process proceeds to step S203, and the average value Avg.(Ttl.N(D_i)) of the total occurrence frequency or frequency for each diagnosis result type is calculated. This average value Avg.(Ttl.N(D_i)) becomes the difficulty calculation reference value for calculating the difficulty.

[0051] Also, in parallel with the extraction of data divided by diagnosis result type in step S202, in step S204, as past data, data narrowed down to the same diagnosis result as the evaluation target is extracted and saved.

[0052] Then, in step S205, the total number or frequency Ttl.N(D_1) of occurrences of the same diagnostic result as the evaluation target is calculated. That is, the total number or frequency of occurrences of past maintenance work identical to the maintenance work performed by the operator this time is obtained.

[0053] Next, in step S206, it is determined whether the total Ttl.N(D_1) is lower than the average value Avg.(Ttl.N(D_i)). If the total Ttl.N(D_1) is lower than the average value Avg.(Ttl.N(D_i)), then in step S207, the difficulty level is increased. More specifically, the lower the total Ttl.N(D_1), that is, the lower the past number or frequency of occurrences of the same maintenance work, the higher the difficulty level of the maintenance work is considered.

[0054] Also, if it is determined in step S206 that the total Ttl.N(D_1) is equal to or higher than the average value Avg.(Ttl.N(D_i)), the process proceeds to step S208 to decrease the difficulty level.

[0055] In another step S209 following step S202, the average value Avg.(Ttl.R(D_i)) of the total number of additional maintenance times for each diagnostic result type is calculated. The average value Avg.(Ttl.R(D_i)) serves as a difficulty calculation reference value for calculating the difficulty level.

[0056] In another step S210 following step S204, the average value Avg.R(X_1) of the number of additional maintenance times for the same diagnostic result as the evaluation target is calculated. This average value Avg.R(X_1) is also used as a difficulty calculation reference value for calculating the difficulty level in step S214 described later.

[0057] Next, in step S211, it is determined whether the average value Avg.R(X_1) is greater than the average value Avg.(Ttl.R(D_i)). If the average value Avg.R(X_1) is greater than the average value Avg.(Ttl.R(D_i)), then in step S212, the difficulty level is increased.

[0058] Also, when it is determined in step S211 that the average value Avg.R(X_1) is less than or equal to the average value Avg.(Ttl.R(D_i)), the process proceeds to step S213 to decrease the difficulty level.

[0059] Also, in another step S214 following step S210, it is determined whether the result R after the current repair and maintenance is less than the average value Avg.R(X_1). That is, it is determined whether the number of additional maintenance times based on the "re-stocking" of the result R after the current repair and maintenance is less than the average value Avg.R(X_1). When the result R after the current repair and maintenance is less than the average value Avg.R(X_1), in step S215, the difficulty level is increased.

[0060] Also, when it is determined in step S214 that the result R after the current repair and maintenance is greater than or equal to the average value Avg.R(X_1), the process proceeds to step S216 to decrease the difficulty level.

[0061] Also, in another step S217 following step S201, data (All type X data) divided by repair and maintenance content is extracted from the repair and maintenance content database 4 as past data. That is, data divided by the repair and maintenance content implemented for all parts of the vehicle is extracted.

[0062] Next, in step S218, the average value Avg.T(X) of the working time for each repair and maintenance content is calculated. This average value Avg.T(X) serves as a difficulty level calculation reference value for calculating the difficulty level.

[0063] Also, in parallel with the extraction of data divided by repair and maintenance content in step S217, in step S219, data narrowed down to the same repair and maintenance content as the evaluation target is extracted as past data.

[0064] Next, in step S220, the average value Avg.T(X_1) of the working hours for the repair and maintenance content that is the same as the evaluation target is calculated. This average value Avg.T(X_1) is also used as a difficulty calculation reference value for calculating the difficulty in step S224.

[0065] Then, in step S221, it is determined whether the average value Avg.T(X_1) is longer than the average value Avg.T(X). If the average value Avg.T(X_1) is longer than the average value Avg.T(X), then in step S222, the difficulty is increased. More specifically, it is assumed that the longer the average value Avg.T(X_1), that is, the average working hours required for the same maintenance work, the higher the difficulty of the maintenance work.

[0066] Also, if it is determined in step S221 that the average value Avg.T(X_1) is less than or equal to the average value Avg.T(X), the process proceeds to step S223 to decrease the difficulty.

[0067] In another step S224 following step S220, it is determined whether the working hours T required by the operator for this repair and maintenance are less than the average value Avg.T(X_1). If the working hours T are less than the average value Avg.T(X_1), then in step S225, the difficulty is increased.

[0068] Also, if it is determined in step S224 that the working hours T are greater than or equal to the average value Avg.T(X_1), the process proceeds to step S223 to decrease the difficulty.

[0069] In the flowchart of FIG. 3, when data with similar correct answer rates for steps S217 and 219 are extracted, the comparison in step S221 can be made more rigorous based on the working accuracy of multiple operators, and the accuracy of calculating the difficulty can be improved.

[0070] Also, in FIG. 3, the method of calculating the difficulty using the references of the failure diagnosis result database 3 and the repair and maintenance content database 4 was described, but the difficulty may be calculated by other calculation methods, for example, a calculation method using machine learning. Further, by weighting the parameters for calculating the difficulty, the difficulty associated with more important parameters can be increased.

[0071] Next, the operator evaluation calculation unit 6 and the operator database 7 will be described while referring to FIG. 1 again.

[0072] The operator evaluation calculation unit 6 obtains an evaluation index for a specific operator in charge of vehicle repair and maintenance within a predetermined period, in this embodiment, an evaluation index based on cumulative evaluation (repair performance 7b of operator evaluation 7a in FIG. 1), considering the difficulty level and contribution degree stored in the repair and maintenance content database 4. This evaluation index based on cumulative evaluation is calculated by cumulatively considering individual evaluations for each repair and maintenance content performed by the specific operator on a specific vehicle within a predetermined period. Here, the individual evaluation for each repair and maintenance content performed by a specific operator on a specific vehicle within a predetermined period, in this embodiment, within one year, can be defined as the "average value of {(P×α + Q×β)×F}". Here, "P" represents the difficulty level, "α" represents the first weighting coefficient multiplied by P, "Q" represents the contribution degree, "β" represents the second weighting coefficient multiplied by Q, and "F" represents the number of pieces of repair and maintenance content performed within one year. When obtaining the above individual evaluation, based on the above definition, for example, when operator A shown in FIG. 1 performs F replacements of EV battery modules as repair and maintenance content within one year, the individual evaluation is calculated. Also, as shown in FIG. 1, operator A shown in FIG. 1 performs two other repair and maintenance contents besides EV battery module replacement, namely oil change and plug (for example, plug replacement), so for these as well, individual evaluations are calculated in the same manner as in the case of EV battery module replacement. Then, the cumulative evaluation for all repair and maintenance contents performed by operator A shown in FIG. 1, that is, the repair performance 7b, is calculated by cumulatively considering the individual evaluations of EV battery module replacement, oil change, and plug respectively. As shown in FIG. 1, the repair performance 7b of operator A is indicated by five stars worthy of the highest evaluation.

[0073] The operator database 7 stores the operator evaluation 7a having the repair performance 7b of each operator calculated by the operator evaluation calculation unit 6 and the customer feedback (customer FB) 7c indicating the reaction from the customer to the repair and maintenance performed by each operator. The customer FB is represented by an evaluation indicated by, for example, stars from 1 to 5. The operator evaluation 7a is displayed on the terminal 8 of the user of the vehicle receiving the maintenance work, for example, a mobile terminal, via the communication unit 1.

[0074] Also, workers without repair records 7b may be added to the worker database 7. This can increase the user's options. Furthermore, by increasing the proportion of workers added to the worker database 7, the evaluation method of the present invention can become the de facto standard, and it can promote the registration of unregistered workers.

[0075] FIG. 4 is a flowchart showing the processing flow of the first embodiment.

[0076] First, in step S300, worker A performs maintenance work.

[0077] Next, in step S301, worker A inputs maintenance work information via the store / worker / date / time input / output unit 2. In this embodiment, worker A inputs the vehicle ID of the vehicle, the repair and maintenance date and time, the worker ID of the worker, the repair and maintenance content, the working hours, the result after repair and maintenance, the store, and the fault diagnosis result via the store / worker / date / time input / output unit 2.

[0078] Then, in step S302, the maintenance work information is recorded in the repair and maintenance content database 4 and the fault diagnosis result database 3. More specifically, the fault diagnosis result is recorded in the fault diagnosis result database 3, and items other than the fault diagnosis result are recorded in the repair and maintenance content database 4.

[0079] Next, in step S303, the repair and maintenance content database 4 and the fault diagnosis result database 3 are referred to.

[0080] Then, in step S304, as past data, the number of occurrences or frequency, repair and maintenance costs, working hours, and correct rate for each type of diagnosis result are extracted. Note that step S304 is based on the above-mentioned steps S102 and steps S103, 110, 112. Also, as described above, in step S102, data with good results after repair and maintenance is extracted, so the correct rate extracted in step S304 is 100%.

[0081] Next, in step S305, based on the number of occurrences or frequency, repair and maintenance costs, and working hours for each type of diagnosis result, a contribution degree calculation reference value is set. This setting corresponds to the above-mentioned steps S104, 111, 113, 120.

[0082] After setting the contribution degree calculation reference value, in step S306, the contribution degree is calculated. This contribution degree is calculated based on the comparison in the above-mentioned steps S107, 114, 117, 121.

[0083] Also, in another step S307 following step S302, the repair and maintenance content database 4 and the failure diagnosis result database 3 are referred to.

[0084] Then, in step S308, as past data, the number of occurrences or frequency, additional maintenance times, working hours, and correct rate for each type of diagnosis result are extracted. Note that step S308 is based on the above-mentioned S202, 204, 217, 219.

[0085] Next, in step S309, based on the number of occurrences or frequency, additional maintenance times, and working hours for each type of diagnosis result, a difficulty level calculation reference value is set. This setting corresponds to the above-mentioned steps S203, 209, 210, 218, 220.

[0086] After setting the difficulty level calculation reference value, in step S310, the difficulty level is calculated. This difficulty level is calculated based on the comparison in the above-mentioned steps S206, 211, 214, 221, 224.

[0087] After the completion of steps S306 and S310, the process proceeds to step S311, where the contribution degree and difficulty level are registered in the repair and maintenance content database 4.

[0088] After this registration, in step S312, the progress of the vehicle on which the repair and maintenance have been performed is monitored.

[0089]

[0090]

[0091]

[0092]

[0093] After calculating the evaluation index, in step S316, the repair record 7b corresponding to the evaluation index is saved in the operator database 7 to update the operator database 7.

[0094] Although Figure 4 states that "the difficulty level and contribution degree are updated each time", it is also possible not to update the difficulty level and contribution degree. For example, although the difficulty level decreases as the number of operations increases and the skills of the operator improve, if it is not desired to reflect this decrease in the operator's work performance, the difficulty level can be set not to be updated.

[0094] As described above, in the first embodiment, the repair and maintenance content database 4 includes, in addition to the vehicle ID of the vehicle, the operator ID of the vehicle, the repair and maintenance content which is the content of the maintenance work, and the repair and maintenance date and time which is the content of the maintenance work, the history of additional maintenance work performed on the same part of the same vehicle after this maintenance work. The fact that additional maintenance work is performed on the same part of the same vehicle after the maintenance work means that the previous maintenance work was an incorrect work, and the additional maintenance work is performed to eliminate this error. By considering the repair and maintenance content database 4 including the additional maintenance work associated with such incorrect work, the relevance between the incorrect maintenance work and the correct additional maintenance work is clarified, and the additional maintenance work is properly evaluated. Therefore, by taking into account the evaluation of this properly evaluated additional maintenance work, an accurate evaluation index for a specific operator can be obtained.

[0095] Also, in the first embodiment, the contribution degree and difficulty calculation unit 5 calculates the difficulty indicating the difficulty of the maintenance work. Then, the operator evaluation calculation unit 6 obtains an evaluation index for a specific operator in consideration of the difficulty calculated by the contribution degree and difficulty calculation unit 5. Therefore, compared with the case where the evaluation of the operator is simply higher as the number of maintenance works is larger, the evaluation index of the operator can be obtained more accurately. For example, if the operator can surely accumulate maintenance works with high difficulty, even if the number of maintenance works is small, a high evaluation index can be obtained.

[0096] Furthermore, in the first embodiment, the contribution degree / difficulty calculation unit 5 calculates the contribution degree indicating the benefits brought by the maintenance work to the vehicle user. Then, the operator evaluation calculation unit 6 obtains an evaluation index for a specific operator in consideration of the contribution degree calculated by the contribution degree / difficulty calculation unit 5. Therefore, compared with the case where the evaluation of the operator is simply higher as the number of maintenance works is larger, the evaluation index of the operator can be obtained more accurately. For example, if the operator can surely accumulate maintenance works with a high contribution degree, even if the number of maintenance works is small, a high evaluation index can be obtained.

[0097] Also, in the first embodiment, after the maintenance work of the vehicle, when there is a re-inventory of the vehicle for additional maintenance work at the same location of the same vehicle, the contribution degree of the corresponding maintenance work in the repair and maintenance content database 4 is corrected to be low. As described above, the additional maintenance work is performed for the incorrect maintenance work that has already been performed, so the incorrect maintenance work has caused a disadvantage to the vehicle user. Therefore, by reducing the contribution degree in consideration of the disadvantage caused by such incorrect maintenance work, the evaluation index for the operator can be obtained more accurately.

[0098] Furthermore, in the first embodiment, it is assumed that the higher the average working time required for the same maintenance work, the higher the difficulty of the maintenance work. Generally, when performing a maintenance work with a long working time, it is presumed that more complex and difficult work is required than normal work, which results in a longer working time. Therefore, by increasing the difficulty in consideration of this complex and difficult work, the evaluation index for the operator can be obtained more accurately.

[0099] Also, in the first embodiment, it is assumed that the lower the past occurrence number or frequency of the same maintenance work, the higher the difficulty level of the maintenance work. Generally, when the past occurrence number or frequency of the maintenance work is low, the history of the maintenance work has not been sufficiently accumulated, and it is necessary to perform the maintenance work based on less information. Therefore, the difficulty level is set higher compared to the case where the history of the maintenance work is rich. Thus, by using the high difficulty level considering the above-mentioned less information, it is possible to more accurately obtain the evaluation index for the operator.

[0100] Furthermore, in the first embodiment, it is assumed that the higher the contribution degree of the maintenance work, the shorter the actual working time T required compared to the average working time required for the same maintenance work. When the actual working time T required is short in this way, it can be inferred that the operator has efficiently performed the maintenance work and brought benefits to the vehicle user. Therefore, by setting the contribution degree high considering this benefit, it is possible to more accurately obtain the evaluation index for the operator.

[0101] Also, in the first embodiment, when the number of parts actually replaced is more than the average number of parts replaced during the same maintenance work, the contribution degree of the maintenance work may be set to be low. When the number of parts actually replaced is large in this way, it can be inferred that another part near the part to be replaced has been replaced together (accompanying replacement), which may cause a disadvantage to the vehicle user. Therefore, by setting the contribution degree low considering this disadvantage, it is possible to more accurately obtain the evaluation index for the operator.

[0102] Furthermore, by more accurately obtaining the evaluation index for the operator by various aspects as described above, it is possible to promote the mobility of operators from other industries to the automotive industry and eliminate the shortage of personnel of operators in the automotive industry.

[0103] FIG. 5 is a functional block diagram of an evaluation device for vehicle maintenance workers in the second embodiment. In addition to the functional block diagram of the evaluation device for vehicle maintenance workers, FIG. 5 shows repair and maintenance content data 4a stored in the repair and maintenance content database 4, worker evaluation 7a and store evaluation 11a that are stored in the worker database 7 and the store database 11 and displayed on the terminal 8.

[0104] The evaluation device for vehicle maintenance workers in the second embodiment is configured by adding an appropriateness calculation unit 9, a store / business trip evaluation calculation unit 10, and a store database 11 to the evaluation device in the first embodiment.

[0105] The appropriateness calculation unit 9 calculates an appropriateness indicating the proximity between the maintenance work performed by the operator this time and the correct maintenance work among the same maintenance work performed by multiple operators in the past. The appropriateness is calculated based on the failure identification degree and the working time. This failure identification degree does not mean the degree of simply identifying the failure of the vehicle parts. Actually, it means the degree of a series of operations until the failure is identified and the maintenance work for this failure is completed. Therefore, regarding the appropriateness based on the failure identification degree, the operator uses the fault diagnostic machine to identify the current failure, and compares a series of operations until the maintenance work performed for this failure with the series of operations where multiple operators identified the same failure and performed the correct maintenance work in the past. The higher the similarity between the two, the higher the appropriateness. When performing this comparison, after extracting data with good results after repair and maintenance from the repair and maintenance content database 4 and the fault diagnosis result database 3, as past data, data narrowed down to the same diagnostic result as the evaluation target and data narrowed down to the same repair and maintenance content as the evaluation target are obtained. Then, for the combination of data narrowed down to the same diagnostic result as the evaluation target and data narrowed down to the same repair and maintenance content as the evaluation target, it is determined to what extent the series of operations until the operator uses the fault diagnostic machine to identify the current failure and complete the maintenance work are similar, that is, to what extent they match. When making this similarity determination, for example, the number of matching points such as the failure content (e.g., engine misfire), the cause of the failure (e.g., injector or plug), the degree of the failure, and the type of replacement parts is considered, and the more matching points there are, the more similar they are considered to be.

[0106] Regarding the appropriateness based on the working time, the shorter the working time of the maintenance work performed by the worker this time is compared to the average value of the working times of the correct maintenance work performed by multiple workers in the past for the same failure, the higher the appropriateness. When calculating this working time, after extracting data with good results after repair and maintenance from the repair and maintenance content database 4 and the failure diagnosis result database 3, as past data, data narrowed down to the same repair and maintenance content as the evaluation target is acquired. Then, the average value of the working times of the correct maintenance work is obtained from this data, and the average value of this working time is compared with the working time of the maintenance work performed by the worker this time.

[0107] The appropriateness calculated based on the failure identification degree and the working time is stored in the repair and maintenance content database 4 in a column provided between the contribution degree and the result after repair and maintenance, as shown in FIG. 5, for example.

[0108] Also, in this embodiment, it may be such that the appropriateness is made lower as the number of parts actually replaced becomes larger than the number of parts replaced in the past in the correct maintenance work among the same maintenance work. The number of parts replaced in the past in the correct maintenance work among the same maintenance work is acquired by referring to the repair and maintenance content database 4.

[0109] Also, in this embodiment, the individual evaluation of one repair and maintenance content performed by a specific worker for a specific vehicle during a predetermined period, which is one year in this embodiment, can be defined as the "average value of {(P×α + Q×β + S×γ)×F}". Here, "P" indicates the difficulty level, "α" indicates the first weighting coefficient multiplied by P, "Q" indicates the contribution degree, "β" indicates the second weighting coefficient multiplied by Q, "S" indicates the appropriateness, "γ" indicates the third weighting coefficient multiplied by S, and "F" indicates the number of one repair and maintenance content performed during one year. Then, by cumulatively considering the individual evaluations of all the repair and maintenance contents performed by a specific worker, an evaluation index based on the cumulative evaluation is calculated.

[0110] Based on the store information input via the store / worker / date / time input / output unit 2, the store / outgoing business evaluation calculation unit 10 calculates the evaluation of the workers for each store. Also, in this embodiment, the store information includes the business trip repairs when traveling to the work location for repairs. As shown in FIG. 5, this evaluation is shown such that, for example, the repair records of one worker in stores X, Y and the business trip repair Z have a maximum evaluation of five stars. Also, as shown in FIG. 5, the customer FB11c is represented by an evaluation indicated by stars from 1 to 5.

[0111] The store database 11 stores the store evaluation 11a including the evaluation 11b of the workers for each store and business trip repair calculated by the store / outgoing business evaluation calculation unit 10 and the customer FB11c.

[0112] Next, with reference to FIG. 6, the processing flow of the second embodiment will be described. In FIG. 6, the same steps as those in the first embodiment are assigned the same numbers.

[0113] First, in step S300, worker A performs maintenance work. In step S301, worker A inputs maintenance work information via the store / worker / date / time input / output unit 2. In step S302, the maintenance work information is recorded in the repair and maintenance content database 4 and the fault diagnosis result database 3.

[0114] Then, in step S303, the repair and maintenance content database 4 and the fault diagnosis result database 3 are referred to. In step S304, the number of occurrences or frequency, repair and maintenance costs, working hours, and correct rate for each type of diagnosis result are extracted as past data. In step S305, a contribution degree calculation reference value is set based on the number of occurrences or frequency, etc. for each type of diagnosis result. In step S306, the contribution degree is calculated.

[0115] Also, in step S307, the repair and maintenance content database 4 and the failure diagnosis result database 3 are referred to. In step S308, the number of occurrences or frequency, the number of additional maintenance times, and the working hours for each type of diagnosis result are extracted as past data. In step S309, a difficulty calculation reference value is set based on the number of occurrences or frequency, etc. for each type of diagnosis result. In step S310, the difficulty is calculated.

[0116] Also, in another step S400 following steps S304 and S308, an appropriateness is calculated based on the failure identification degree and the working hours. At the time of this calculation, as described above, the degree of matching between the combination of the data narrowed down to the same diagnosis result as the evaluation target and the data narrowed down to the same repair and maintenance content as the evaluation target, and the series of operations for identifying the current failure and performing maintenance work is determined. Further, the average value of the working hours of the correct maintenance work is compared with the working hours of the maintenance work performed by the operator this time.

[0117] After calculating the contribution degree, the difficulty, and the appropriateness, in step S401, the contribution degree, the difficulty, and the appropriateness are registered in the repair and maintenance content database 4.

[0118] Then, in step S312, the progress of the vehicle on which the repair and maintenance has been performed is monitored. In step S313, it is determined whether additional maintenance work has occurred within a predetermined period, which is one year in this embodiment. If additional maintenance work has occurred within the predetermined period, in step S314, the contribution degree for the corresponding maintenance work in the repair and maintenance content database is corrected downward. Then, in step S402, an evaluation index for operator A is calculated based on the contribution degree, the difficulty, and the appropriateness. In step S316, the operator database 7 is updated.

[0119] Also, if it is determined in step S313 that no additional maintenance work has occurred within the predetermined period, the process proceeds to step S402, and an evaluation index for operator A is calculated based on the contribution degree, the difficulty, and the appropriateness.

[0120] After the update of the operator database 7, in step S403, the evaluation of the operator for each store is calculated.

[0121] Next, in step S404, the evaluation of the operator for each store is registered in the store database 11.

[0122] In addition, in FIG. 6, the method for calculating the appropriateness using the references of the failure diagnosis result database 3 and the repair and maintenance content database 4 has been described. However, the appropriateness may be calculated by other calculation methods, for example, a calculation method using machine learning. Also, by weighting the parameters for calculating the appropriateness, the appropriateness associated with more important parameters can be increased.

[0123] As described above, in the second embodiment, the appropriateness calculation unit 9 calculates the appropriateness indicating the proximity between the maintenance work and the correct maintenance work among the maintenance works identical to this maintenance work. Then, the operator evaluation calculation unit 6 obtains the evaluation index of a specific operator in consideration of the appropriateness calculated by the appropriateness calculation unit 9. Therefore, the evaluation index of the operator can be obtained more accurately compared to the case where the evaluation of the operator is simply increased as the number of maintenance works increases.

[0124] Also, in the second embodiment, the appropriateness may be decreased as the number of actually replaced parts becomes larger than the number of parts replaced in the past in the correct maintenance work among the same maintenance works. In such a case where the number of actually replaced parts increases, it can be presumed that, in addition to the parts to be replaced, parts that should not have been replaced were replaced together (accompanying replacement), and it becomes far from the correct maintenance work. Therefore, by considering the accompanying replacement and decreasing the appropriateness, the evaluation index of the operator can be obtained more accurately.

[0125] FIG. 7 is a functional block diagram of an evaluation device for vehicle maintenance workers in the third embodiment. In FIG. 7, in addition to the functional block diagram of the evaluation device for vehicle maintenance workers, repair and maintenance content data 4a stored in the repair and maintenance content database 4, worker evaluation 7a and store evaluation 11a stored in the worker database 7 and the store database 11, and displayed on the terminal 8 are shown.

[0126] The evaluation device for vehicle maintenance workers in the third embodiment is configured by adding a calculation criterion change unit 12 at the time of part replacement and a work manual database 13 to the evaluation device in the second embodiment. Further, in the third embodiment, the repair and maintenance content database 4 stores part replacement data indicating whether there has been an update of the parts replaced in the repair and maintenance content.

[0127] When the maintenance work is a part replacement work, the calculation criterion change unit 12 at the time of part replacement distinguishes whether the replaced part is an existing model or a new model. If the distinguished result is a new model, the difficulty calculation criterion value is set relatively high, and thereby, the difficulty of the maintenance work based on this high difficulty calculation criterion value is also relatively high.

[0128] The work manual database 13 stores work manuals for performing each repair and maintenance content. By referring to this work manual database 13, it is possible to determine whether there is a description in the work manual. When there is no description in the work manual, the difficulty calculation criterion value is set relatively higher than when there is such a description, and thereby, the difficulty of the maintenance work based on this high difficulty calculation criterion value is also set relatively higher.

[0129] FIG. 8 is a flowchart showing the processing flow of the third embodiment. In FIG. 8, the same steps as those in the second embodiment are assigned the same numbers.

[0130] First, in step S300, worker A performs maintenance work. In step S301, worker A inputs maintenance work information via the store / worker / date and time input / output unit 2. In step S302, the maintenance work information is recorded in the repair and maintenance content database 4 and the failure diagnosis result database 3.

[0131] Then, in step S303, the repair and maintenance content database 4 and the failure diagnosis result database 3 are referred to. In step S304, the number of occurrences or frequency, repair and maintenance costs, working hours, and correct rate for each type of diagnosis result are extracted as past data. In step S305, a contribution degree calculation reference value is set based on the number of occurrences or frequency, etc. for each type of diagnosis result. In step S306, the contribution degree is calculated.

[0132] Also, in parallel with step S303, in step S500, the repair and maintenance content database 4, the failure diagnosis result database 3, and the work manual database 13 are referred to.

[0133] Next, in step S501, the number of occurrences or frequency, additional maintenance times, working hours, correct rate, presence or absence of work manual description, and parts update data for each type of diagnosis result are extracted from the repair and maintenance content database 4 and the work manual database 13.

[0134] Although not shown in the figure, in step S501, the presence or absence of work manual description is determined. When there is no work manual description, the difficulty calculation reference value is made relatively higher compared to the case where there is such a description. As a result, the difficulty of the maintenance work based on this high difficulty calculation reference value also becomes relatively higher.

[0135] After the extraction of the number of occurrences or frequency, etc. for each type of diagnosis result in step S501, a difficulty calculation reference value is set in step S309, and the difficulty is calculated in step S310.

[0136] Also, in another step S502 following step S501, a parts replacement operation is performed to determine whether the replaced part is a new model. If it is a new model, the process proceeds to step S503, where the difficulty calculation reference value is made relatively high.

[0137] Also, if it is determined in step S502 that the replaced part is not a new model when performing the parts replacement operation, assuming that the replaced part is an existing model, the process proceeds to step S309 without changing the difficulty calculation reference value.

[0138] Also, in another step S400 following steps S304 and 501, an appropriateness is calculated based on the failure identification degree and the working time.

[0139] After calculating the contribution degree, difficulty, and appropriateness, in step S401, the contribution degree, difficulty, and appropriateness are registered in the repair and maintenance content database 4. In step S312, the progress of the vehicle for which repair and maintenance have been performed is monitored. In step S313, it is determined whether additional maintenance work has occurred within a predetermined period, which is one year in this embodiment. If additional maintenance work has occurred within the predetermined period, in step S314, the contribution degree for the corresponding maintenance work in the repair and maintenance content database is corrected to be low. In step S402, an evaluation index for operator A is calculated based on the contribution degree, difficulty, and appropriateness. In step S316, the operator database 7 is updated.

[0140] Also, if it is determined in step S313 that no additional maintenance work has occurred within the predetermined period, the process proceeds to step S402, where an evaluation index for operator A is calculated based on the contribution degree, difficulty, and appropriateness.

[0141] After updating the operator database 7, in step S403, an evaluation of the operator for each store is calculated. In step S404, the evaluation of the operator for each store is registered in the store database 11.

[0142] As described above, in the third embodiment, when the maintenance work is a component replacement work, the calculation standard change unit 12 at the time of component update distinguishes whether the replaced component is an existing model or a new model. If the result of this distinction is a new model, the difficulty calculation standard value is made relatively high. Then, the difficulty calculated based on this high difficulty calculation standard value is also relatively high. Since the components of the new model have less information regarding maintenance work than the components of the existing model, it is presumed that more time and effort are required for maintenance work, making the maintenance work more difficult. Therefore, by relatively increasing the difficulty of the maintenance work in the case of the new model in consideration of such a situation, the evaluation index of the operator can be obtained more accurately.

Explanation of Signs

[0143] 1 ··· Communication unit 2 ··· Store / Operator / Date and Time Input / Output Unit 3 ··· Failure Diagnosis Result Database 4 ··· Repair / Maintenance Content Database 5 ··· Contribution Degree / Difficulty Calculation Unit 6 ··· Operator Evaluation Calculation Unit 7 ··· Operator Database 8 ··· Terminal 9 ··· Appropriateness Calculation Unit 10 ··· Store / Business Trip Evaluation Calculation Unit 11 ··· Store Database 12 ··· Calculation Standard Change Unit at the Time of Component Update 13 ··· Work Manual Database

Claims

1. Create a repair and maintenance content database that includes the vehicle ID of the vehicle targeted for maintenance work, the date and time when the maintenance work was performed, the operator ID of the operator who performed the maintenance work, the content of the maintenance work, and the history of additional maintenance work performed on the same location of the same vehicle after this maintenance work. Based on this repair and maintenance content database, determine evaluation criteria for a specific operator. A method for evaluating vehicle maintenance workers.

2. When performing maintenance work, calculate the difficulty level indicating the difficulty of the maintenance work. Include the data of this difficulty level in the above repair and maintenance content database. The method for evaluating a vehicle maintenance worker according to claim 1, further comprising determining the above evaluation criteria in consideration of this difficulty level.

3. When performing maintenance work, calculate the contribution degree indicating the benefit brought to the vehicle user by the maintenance work. Include the data of this contribution degree in the above repair and maintenance content database. The method for evaluating a vehicle maintenance worker according to claim 1, further comprising determining the above evaluation criteria in consideration of this contribution degree.

4. When there is the above additional maintenance work, the method for evaluating a vehicle maintenance worker according to claim 3 further comprises lowering the contribution degree for the corresponding maintenance work in the above repair and maintenance content database.

5. Based on the above repair and maintenance content database, determine the average working time required for the same maintenance work. The method for evaluating a vehicle maintenance worker according to claim 2, further comprising assuming that the higher the average working time, the higher the difficulty level of the maintenance work.

6. Based on the above repair and maintenance content database, determine the past occurrence times or frequency of the same maintenance work. The method for evaluating a vehicle maintenance worker according to claim 2, further comprising assuming that the lower the occurrence times or frequency, the higher the difficulty level of the maintenance work.

7. When the maintenance work is a parts replacement work, distinguish whether the replaced part is an existing model or a new model. The method for evaluating a vehicle maintenance worker according to claim 2, further comprising assuming that the difficulty level of the maintenance work is relatively high for a new model.

8. Referring to the repair and maintenance content database, obtaining the average working time required for the same maintenance work, The method for evaluating a vehicle maintenance worker according to claim 3, further comprising assuming that the higher the contribution degree of the maintenance work, the shorter the actual working time compared to this average working time.

9. Referring to the repair and maintenance content database, obtaining the average number of parts replaced during the same maintenance work, The method for evaluating a vehicle maintenance worker according to claim 3, further comprising assuming that when the actual number of parts replaced is larger than this average number of parts, the contribution degree of the maintenance work is low.

10. When performing a maintenance work, calculating an appropriateness indicating the proximity between the maintenance work and the correct maintenance work among the same maintenance works as this maintenance work, Including the data of this appropriateness in the repair and maintenance content database, The method for evaluating a vehicle maintenance worker according to claim 1, further comprising obtaining the evaluation index in consideration of this appropriateness.

11. When the maintenance work is a parts replacement work, obtaining the number of parts replaced during the correct maintenance work among the same maintenance works, The method for evaluating a vehicle maintenance worker according to claim 10, characterized in that the higher the actual number of parts replaced compared to this number of parts, the lower the appropriateness of the maintenance work.

12. The method for evaluating a vehicle maintenance worker according to claim 1, further comprising displaying the evaluation index of the assigned worker on the mobile terminal of the user of the vehicle receiving the maintenance work.

13. A repair and maintenance content storage unit including a vehicle ID of the vehicle subject to the maintenance work, the date and time when the maintenance work was performed, the worker ID of the worker who performed the maintenance work, the content of the maintenance work, and the history of additional maintenance work performed on the same part of the same vehicle after this maintenance work; An evaluation calculation unit that calculates an evaluation index for a specific worker with reference to the repair and maintenance content storage unit; An evaluation device for a vehicle maintenance worker provided with the above.

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