A data processing and management method and system for industrial vehicle operations

Abstract

The present invention relates to a data processing and management method and system for industrial vehicle operations. The data processing method includes the following steps: First, data classification: all data is classified into four categories of "very important", "important", "general", and "negligible" according to the importance of the data. Second, data cleaning: used to process abnormal data to fill or re-obtain the corresponding data. Third, data derivation: used to classify the cleaned data to form a driver dataset, an industrial vehicle dataset, and a single operation dataset, and then generate a single operation evaluation, an industrial vehicle evaluation, and a driver evaluation based on the data in each dataset. By classifying various data generated during the operation of industrial vehicles, deleting the data with lower importance, reducing the pressure of data storage and analysis, improving the effectiveness of data, enhancing the rationality of data analysis, and further improving the supervision intensity of industrial vehicle operations.

Description

Technical Field

[0001] The present invention relates to a data processing method, in particular to a data processing method for industrial vehicle operations, a management method for industrial vehicle operations, and a management system for industrial vehicle operations. Background Art

[0002] With the development of the industrial level, the number of industrial vehicles in use is also increasing day by day. Subsequently, the number of industrial vehicle accidents remains high, showing an upward trend for several consecutive years, and industrial vehicle accidents have received more extensive attention. In order to improve the operation safety of industrial vehicles and the supervision ability of industrial vehicles, an industrial vehicle safety monitoring and management system has emerged as the times require.

[0003] In the prior art, there are still some defects in the monitoring and early warning of industrial vehicles: 1. During the operation of the vehicle, it is difficult to monitor various data of the vehicle in real time, and the early warning delay is relatively high, resulting in untimely vehicle early warning, inability to process the early warning situation in a timely and effective manner, and frequent accidents. 2. If a large amount of data during the operation of industrial vehicles needs to be remotely monitored in real time, the pressure on the remote server is relatively large, which may cause the situation where useful data is blocked by useless data, and a large amount of useless data is stored in the database, while useful data cannot be received. 3. The historical distribution of data is unreasonable, and there is a large amount of data missing during accidents, making it impossible to trace the cause of the accident. Summary of the Invention

[0004] Based on this, in view of the problem that when detecting industrial vehicle operations in the prior art, due to data redundancy, the data processing efficiency is low, and it is difficult to supervise industrial vehicles in a timely and effective manner, it is necessary to provide a data processing and management method and system for industrial vehicle operations.

[0005] The present invention is realized through the following technical solutions: A data processing method for industrial vehicle operations includes the following steps:

[0006] I. Data classification: According to the importance of the data, all data are divided into four categories: "very important", "important", "general", and "negligible". Among them, the importance ρ of each item of data i is expressed as:

[0007]

[0008] ω S +ω E +ω C +ω F = 1

[0009] In the formula, Score i (S) is the safety score, ω Sis the weight of the safety score, Score i (E) is the efficiency score, ω E is the weight of the efficiency score, Score i (C) is the cost score, ω C is the weight of the cost score, Score i (F) is the fault score, ω F is the weight of the fault score.

[0010] II. Data cleaning includes missing value processing, abnormal format processing, abnormal content processing, duplicate data processing, and abnormal data processing. Missing value processing is only for data with missing values. The methods for missing value processing are as follows: ①. Directly remove the missing data with the category of "negligible" and mark the missing values. ②. Fill in the missing data with the category of "general" by fitting, and fill in the missing data with the category of "important" by prediction, and mark the filling respectively. ③. For the missing data with the category of "very important", data needs to be obtained again. If there are still missing values, a missing alert is issued. Abnormal format processing is used to mark the data whose format does not conform to a preset storage format, and then perform missing value processing according to the importance of the data. Abnormal content processing is used to mark the data whose content exceeds a preset reasonable range, and then perform missing value processing according to the importance of the data. Duplicate data processing is used to calculate the hash value of each data and determine whether there are data with the same hash value within a time period. If so, keep one of the data and delete the other data with the same hash value. Abnormal data processing is only for feature data and location information, and is used to mark the data that is obviously abnormal in feature data and location information as abnormal data and issue an abnormal warning.

[0011] III. Data derivation is used to classify the cleaned data to form a driver dataset, an industrial vehicle dataset, and a single operation dataset, and then generate single operation evaluations, industrial vehicle evaluations, and driver evaluations based on the data in each dataset.

[0012] The above data processing method classifies various data generated during the operation of industrial vehicles, deletes the data with lower importance, and reduces the pressure of data storage and analysis. At the same time, by marking the missing or abnormally content data, and then processing according to the importance of the corresponding data, such as fitting filling, prediction filling, obtaining again, etc., and issuing an alert when there are still abnormal data to remind the relevant operators to process, improve the effectiveness of the data, enhance the rationality of data analysis, and then improve the supervision intensity of industrial vehicle operations.

[0013] In one embodiment, the single - job evaluation includes single - path quality evaluation, single - ideal - job energy - consumption evaluation, and single - job energy - consumption index evaluation. The single - path quality evaluation is expressed as:

[0014] F(R r ,R i ) = max(min(d(p1,q1),d(p2,q2),…,d(p k ,q k ))); i = 1, 2, 3, ……, k.

[0015] In the formula, R r is the single - trip driving path, p1, p2, …, p k are the points on R r respectively, R i is the ideal driving path, q1, q2, …, q k are the points on R i respectively, d(p i ,q i ) is the Euclidean distance between p i and q i , and F(R e ,R i ) is the Fréchet distance between the two paths, representing the maximum matching degree of the two paths.

[0016] The single - job energy - consumption index evaluation ρ e is expressed as:

[0017]

[0018] E r is the single - job fuel loss, and E i is the single - ideal - job energy - consumption.

[0019] In one embodiment, the industrial - vehicle evaluation includes vehicle - engine loss - index evaluation and vehicle - overall loss - index evaluation. The vehicle - engine loss - index evaluation ρ p is expressed as:

[0020]

[0021] In the formula, E ij is the sum of the single - ideal - job energy - consumptions of the industrial vehicle between two refuelings, Es is the total fuel loss of the industrial vehicle between two refuelings, and n is the total number of the single - ideal - job energy - consumptions of the industrial vehicle between two refuelings.

[0022] The vehicle - overall loss - index evaluation ρ v is expressed as:

[0023]

[0024] Wherein, P i is the ideal value of the tire pressure, P tj is the tire pressure, n v is the number of tires, m is the number of abnormal accessory states, and M is the number of accessory failures.

[0025] In one embodiment, the driver evaluation includes the driver's single-operation quality index and the driver's operation quality index. The driver's single-operation quality index ρ d is expressed as:

[0026]

[0027] Wherein, ρ l is the single-path quality index, and n d is the number of warnings in the single-operation driving data.

[0028] The driver's operation quality index ρ D is expressed as:

[0029]

[0030] Wherein, n D is the total number of operations.

[0031] The present invention also provides a data processing method for industrial vehicle operations, including the following steps:

[0032] S1: Identify the driver. After successful identification, unlock the industrial vehicle; otherwise, lock the industrial vehicle. After unlocking, the industrial vehicle can be normally started and driven. After locking, the industrial vehicle cannot be started.

[0033] S2: After the industrial vehicle is started, perform a self-check on the industrial vehicle. The self-check method is as follows: S21: Collect the characteristic data and load data of the industrial vehicle. S22: Determine whether the characteristic data and load data exceed a preset index. If so, lock the industrial vehicle and issue an alarm. S23: Calculate the vehicle's center-of-gravity coordinates after loading based on the load data and characteristic data. Determine whether the vehicle's center-of-gravity coordinates exceed a preset coordinate range. If so, lock the industrial vehicle and issue an alarm.

[0034] S3: When the industrial vehicle is in a driving state, the driving state, position information, and characteristic data of the vehicle are monitored in real time to enable the industrial vehicle to drive safely within a preset moving range. The method for real-time monitoring of the driving state and position information is as follows: S31: Collect the driving data and position information of the vehicle. The driving data includes the driving speed of the industrial vehicle. S32: Determine whether the driving speed is higher than a preset speed threshold. If so, issue a deceleration warning. S33: Calculate the current acceleration of the industrial vehicle based on the driving speed, and determine whether the acceleration exceeds a preset acceleration threshold. If so, issue a deceleration warning. S34: Determine whether the position information exceeds a preset position range. If so, issue a warning to pay attention to the driving direction. Continue to determine whether the position information returns to the position range within a preset time period t1. If it does not return, turn off the industrial vehicle and send a return warning to the management personnel.

[0035] S4: Monitor the parking of the industrial vehicle to enable the industrial vehicle to be parked in a preset parking area.

[0036] S5: Adopt the above data processing method for industrial vehicle operations, and generate corresponding single-operation evaluations, industrial vehicle evaluations, and driver evaluations based on the characteristic data, load data, driving data, and position information.

[0037] In one embodiment, the driver's identity is identified by the following method:

[0038] S11: The driver inputs a registered account number, and searches for the corresponding driver data in a pre-stored identity database according to the account number. The driver data includes the account number and the driver's photo.

[0039] S12: Collect the frontal photo of the driver, and determine whether the frontal photo matches the corresponding driver photo through a pre-stored face recognition model. If so, it indicates successful identification.

[0040] In one embodiment, the calculation method of the vehicle's center of gravity coordinates is as follows:

[0041] S231: Establish a three-dimensional coordinate system with the center of gravity of the industrial vehicle in the unloaded state as the origin.

[0042] S232: Obtain the weight data of the driver, simulate the sitting posture of the driver in the driving state, and calculate the vehicle's center of gravity coordinates when the driver is in the driving state.

[0043] S233: Map the load data into the three-dimensional coordinate system to obtain the load center of gravity, and then calculate the final overall center of gravity coordinates in combination with the vehicle's center of gravity.

[0044] In one embodiment, the calculation method of the acceleration is as follows:

[0045] S331: Collect multiple recent driving speeds according to a preset time period t2.

[0046] S332: Fit multiple driving speeds to obtain an acceleration function, and then take the derivative of the acceleration function to obtain the current acceleration of the industrial vehicle.

[0047] The present invention also provides a management system for industrial vehicle operations. The management system includes: multiple industrial vehicles, a server, and a mobile terminal.

[0048] The industrial vehicle includes a detection device. The detection device is used to monitor in real time the characteristic data, load data, driving data, and position information of the industrial vehicle.

[0049] The server is remotely connected to each industrial vehicle. The server is used for: First, encode each industrial vehicle. Second, perform data interaction with each industrial vehicle, receive and store the characteristic data, load data, driving data, and position information. Third, generate corresponding single-operation evaluations, industrial vehicle evaluations, and driver evaluations according to the characteristic data, load data, driving data, and position information of each industrial vehicle.

[0050] The mobile terminal is remotely connected to the server. The mobile terminal is used to perform data interaction with the server, and thus realize remote control of the industrial vehicle.

[0051] In one embodiment, the industrial vehicle further includes a controller, and the controller includes an identification module, an edge computing module, a warning signal generation module, and a control signal generation module.

[0052] The identification module includes an account identification module and a face recognition module. The account identification module is used to determine whether the username and password entered by the driver match. If the match is successful, the corresponding driver data is obtained; otherwise, an identification failure signal is generated. The driver data includes a face photo and weight data. The face recognition module is used to determine whether the front photo of the driver collected in real time matches the corresponding face photo. If the match is successful, an unlock signal is generated; otherwise, an identification failure signal is generated.

[0053] The edge computing module is used for: First, establish a three-dimensional coordinate system with the center of gravity of the industrial vehicle in the empty vehicle state as the origin, and calculate the center of gravity coordinates of the whole vehicle after loading the industrial vehicle in combination with the load data and driver data. Second, select multiple driving speeds within the previous time period t2 from the driving data, fit the multiple driving speeds to obtain an acceleration function, and take the derivative of the acceleration function to obtain the current acceleration of the industrial vehicle.

[0054] The warning signal generation module is used for: 1. When the recognition module generates recognition failure signals three times in a row, generating a warning signal for the driver. 2. When the feature data exceeds a preset index, generating a self-check warning signal. 3. When the vehicle's center of gravity coordinates exceed a preset coordinate range, generating a warning signal for the load. 4. When the driving speed exceeds a preset speed threshold or the acceleration exceeds a preset acceleration threshold, generating a deceleration warning signal. 5. When the position information exceeds a preset position range, generating a return warning signal.

[0055] The control signal generation module is used to generate a vehicle locking signal when the warning signal generation module generates a warning signal for the driver, a self-check warning signal, or a warning signal for the load.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The data processing method of the present invention classifies various data generated during the operation of industrial vehicles, deletes the data with relatively low importance, reduces the pressure of data storage and analysis. At the same time, by marking the missing or abnormally content data, and then processing according to the importance of the corresponding data, such as fitting filling, predictive filling, re-acquisition, etc., and alarming the still abnormal data to remind relevant operators to process, improving the effectiveness of the data, enhancing the rationality of data analysis, and further improving the supervision intensity of industrial vehicle operations.

[0058] 2. The management method of the present invention verifies the identity of the driver, and during the operation process, real-time monitors the feature data, load data, driving data, and position information of the industrial vehicle, and timely reminds the driver when the data is abnormal, which is convenient for timely maintenance or adjustment to ensure driving safety. In addition, various data generated during the operation process are integrated to form a reference report for the convenience of management personnel to view, and used as a reference for future operation task planning and vehicle safety management, improving operation efficiency and reducing accident risks.

[0059] 3. The management method of the present invention adopts two-level identity verification, that is, the account verification and face verification of the driver, which can ensure the correct identity of the driver, prevent the industrial vehicle from being randomly called, improve the management order, and reduce safety risks.

[0060] 4. The management method of the present invention monitors the vehicle parking to enable the driver to develop the habit of standard parking, which can not only improve the neatness and beauty of vehicle parking, but also improve the parking efficiency and facilitate the passage of other vehicles or personnel. For electric industrial vehicles, parking the industrial vehicle in the corresponding parking area can also facilitate timely charging. Brief Description of the Drawings

[0061] Figure 1 Flow chart of the management method for the operation of an industrial vehicle according to Embodiment 1 of the present invention;

[0062] Figure 2 is Figure 1 Flow chart of the driver identification method in

[0063] Figure 3 is Figure 1 Flow chart of the vehicle self-check method in

[0064] Figure 4 is Figure 1 Flow chart of the driving monitoring method in

[0065] Figure 5 is Figure 1 Step diagram of the data processing method in

[0066] Figure 6 is adopted Figure 1 Structural schematic diagram of the management system for the management method of the operation of an industrial vehicle in

[0067] Figure 7 is Figure 6 Modular structural schematic diagram of an industrial vehicle in Specific implementation mode

[0068] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0069] It should be noted that when a component is referred to as being "installed on" another component, it can be directly on the other component or there may also be an intermediate component. When a component is considered to be "set on" another component, it can be directly set on the other component or there may be an intermediate component at the same time. When a component is considered to be "fixed to" another component, it can be directly fixed to the other component or there may be an intermediate component at the same time.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0071] Embodiment 1

[0072] Please refer to Figure 1 , which is a flowchart of the management method for the operation of industrial vehicles in Embodiment 1 of the present invention. The management method for the operation of industrial vehicles includes the following steps:

[0073] S1: The industrial vehicle is parked in the parking area and in the flameout state. The driver initially unlocks the industrial vehicle by means of a vehicle key or fingerprint recognition, etc., and then opens the door and enters the cab. The identity of the driver is recognized. After successful recognition, the industrial vehicle is further unlocked so that the industrial vehicle can be started and operated normally. Otherwise, the industrial vehicle is locked. In the locked state, the industrial vehicle cannot be started, and at the same time, the door is locked, and the driver cannot enter or exit at will.

[0074] Please combine with Figure 2 , which is Figure 1 a flowchart of the driver identity recognition method. The driver identity is recognized by the following method:

[0075] S11: When the driver first uses the industrial vehicle for operation, the driver enters his own data into the industrial vehicle system and matches the corresponding account. After the driver enters the cab, the display screen of the industrial vehicle shows a login interface. The driver enters the registered account, and the account includes a user name and a password, and then searches for the corresponding driver data in a pre-stored identity database according to the entered account. Among them, the driver data includes the account, the driver's photo, and may also include the driver's weight, body shape, age, job position, etc.

[0076] S12: After the account login is successful, the driver faces the recognition lens, and then the front photo of the driver is collected through the recognition lens. It is judged whether the front photo matches the corresponding driver photo through a pre-stored face recognition model. If so, it indicates successful recognition. In this embodiment, the face recognition model uses the face_recognition algorithm to calculate the Euclidean distance between the collected front photo and the driver photo. If the Euclidean distance is higher than a preset threshold, the recognition is successful. Of course, in other embodiments, the face recognition model can also use algorithms such as Eigenfaces algorithm and FisherFace algorithm to judge whether the driver is recognized successfully.

[0077] In practical applications, if the driver fails to recognize successfully three times in a row, the industrial vehicle will be locked. For example, when the driver fails to log in successfully three times in a row due to entering the wrong account number, or fails to recognize the face successfully three times in a row after successful login, the industrial vehicle will be locked and an alarm will be sent to the management personnel. At this time, the driver can communicate with the management personnel through the communication tool set on the industrial vehicle, such as making a video call with the management personnel. If the management personnel confirm that the driver is correct, the alarm can be turned off and the driver can re-authenticate. If the authentication is successful, the industrial vehicle will be unlocked so that the driver can perform operations. If the authentication is still unsuccessful, the vehicle will be marked as faulty and the management personnel will carry out emergency handling, such as replacing the industrial vehicle, repairing the industrial vehicle, etc.

[0078] Please combine Figure 3 , which is Figure 1 the flowchart of the vehicle self-checking method in

[0079] S2: After the industrial vehicle starts, self-check the industrial vehicle. The self-checking method is as follows:

[0080] S21: Corresponding monitoring components are installed on each industrial vehicle. When the industrial vehicle starts, the monitoring components are powered on and start to collect the characteristic data and load data of the industrial vehicle. Among them, if the industrial vehicle is a fuel vehicle, the characteristic data includes the fuel quantity of the industrial vehicle, as well as the engine temperature, tire pressure, accessory status, etc. If the industrial vehicle is an electric vehicle, the characteristic data should also include the battery charge, voltage, etc. The load data includes the overall height, width, length and weight of the load, and can also include the variety, packaging, etc. of the load.

[0080] S22: Determine whether the characteristic data and load data exceed a preset index. If so, lock the industrial vehicle and issue an alarm. Otherwise, the industrial vehicle can be put into normal operation.

[0081] Among them, if the engine temperature, tire pressure, accessory status, etc. of the industrial vehicle are abnormal, that is, exceed the index, a warning will be sent to the management personnel to remind the relevant personnel to carry out maintenance. When the industrial vehicle is a fuel vehicle, judge whether the fuel quantity is lower than a preset fuel quantity threshold. If so, issue a refueling alarm to remind the management personnel to refuel. Specifically, when the management personnel receive the refueling alarm, they can send an instruction to allow refueling to the industrial vehicle so that the industrial vehicle can determine the refueling route, and the driver drives the industrial vehicle to the corresponding refueling station for refueling. When the industrial vehicle is an electric vehicle, judge whether the battery charge is lower than a preset battery charge threshold. If so, issue a charging alarm. When both the driver and the management personnel receive the charging alarm, the driver can directly charge the industrial vehicle in the parking space. The management personnel can then reallocate the vehicle for the driver to continue the operation.

[0082] If the length, width, height or weight of the load exceeds the preset indicators, a corresponding load alarm is issued, and the industrial vehicle is locked. The driver adjusts the goods. After the preset indicators are met, the alarm is automatically cancelled and the industrial vehicle is unlocked again.

[0083] S23: Calculate the center-of-gravity coordinates of the whole vehicle after loading the industrial vehicle based on the load data and characteristic data. Determine whether the center-of-gravity coordinates of the whole vehicle exceed a preset coordinate range. If so, lock the industrial vehicle and issue an alarm. After receiving the alarm, the driver independently adjusts the load position or coordinates with the corresponding logistics personnel to adjust the load position until the center-of-gravity coordinates of the whole vehicle are within the preset coordinate range, then the alarm is automatically cancelled and the industrial vehicle is unlocked again.

[0084] Among them, the calculation method of the center-of-gravity coordinates of the whole vehicle is as follows:

[0085] S231: Establish a three-dimensional coordinate system with the center of gravity of the industrial vehicle in the unloaded state as the origin. Specifically, by querying or measuring the body size and weight of the industrial vehicle, map the industrial vehicle into a three-dimensional coordinate system, and then calculate the center-of-gravity coordinates of the industrial vehicle in the unloaded state, and move this center of gravity to the origin of the three-dimensional coordinate system.

[0086] S232: Obtain the weight data of the driver, simulate the sitting posture of the driver in the driving state, and calculate the center-of-gravity coordinates of the vehicle when the driver is in the driving state. When the driver is in the driving state, the center position may change due to driving actions. Therefore, when calculating the center-of-gravity coordinates of the vehicle in the driving state, the corresponding center-of-gravity coordinate range of the vehicle should be calculated according to the possible center change range of the driver. If the industrial vehicle is a heavy vehicle, when the driver's weight is less than 1 / 20 of the weight of the industrial vehicle, the center-of-gravity change of the driver can be ignored, and the center-of-gravity coordinates of the vehicle can be calculated by simulating the common posture of the driver.

[0087] S233: Map the load data into the three-dimensional coordinate system to obtain the load center of gravity, and then calculate the final overall center-of-gravity coordinates in combination with the center of gravity of the whole vehicle. The goods carried by the industrial vehicle each time are generally similar to regular shapes, such as cuboids, cylinders, etc. The load center of gravity can be calculated based on the overall height, width, length, etc. of the goods and the total weight of the goods.

[0088] Please combine Figure 4 , which is Figure 1Flowchart of the driving monitoring method. S3: When the industrial vehicle is in the driving state, the driving state, position information and characteristic data of the vehicle are monitored in real time, so that the industrial vehicle can drive safely within a preset moving range. During the driving process of the industrial vehicle, the monitoring of the characteristic data is still maintained. For example, when the fuel level is too low, a warning is still issued to remind the driver to refuel as soon as possible to ensure the normal operation of the industrial vehicle. Or when there is an abnormal accessory state, the industrial vehicle should be parked in a safe area in time, waiting for maintenance or self-checking for faults until the industrial vehicle returns to normal.

[0089] The method for real-time monitoring of the driving state and position information is as follows:

[0090] S31: Collect the driving data and position information of the vehicle. The driving data includes the driving speed of the industrial vehicle, and may also include the driving direction of the vehicle, the front wheel angle, the whole vehicle deflection direction, and the road conditions, etc. The position information can be obtained by real-time positioning with a GPS installed on the industrial vehicle.

[0091] S32: Judge whether the driving speed is higher than a preset speed threshold. If so, a deceleration warning is issued. The speed threshold can be set separately according to the working area of the industrial vehicle. For example, when in the working area, since there are many staff and there are also many industrial vehicles or other equipment coming and going, the speed threshold can be set to 10 km / h to avoid accidents. When the industrial vehicle is on a public road, the corresponding speed threshold can be set according to the speed limit value of the current road, such as set to 60 km / h, etc. If the industrial vehicle exceeds the speed threshold for a long time, such as not decelerating within 10 s, or not dropping below the speed threshold within 30 s, then the industrial vehicle is forced to decelerate or even directly shut down to avoid accidents.

[0092] S33: Calculate the current acceleration of the industrial vehicle according to the driving speed, and judge whether the acceleration exceeds a preset acceleration threshold. If so, a deceleration warning is issued. As mentioned above, when the industrial vehicle issues a deceleration warning for a long time, then the industrial vehicle is directly forced to decelerate or shut down. The calculation method of the acceleration is as follows:

[0093] S331: Collect multiple driving speeds with the most recent time according to a preset time period t2. For example, collect the current driving speed every 1 s, and collect 6 consecutive driving speeds in total.

[0094] S332: Fit multiple driving speeds to obtain an acceleration function, and then take the derivative of the acceleration function to obtain the acceleration of the current industrial vehicle. Specifically, multiple collected driving speeds can be mapped in a plane coordinate system to form multiple coordinate points, and the multiple coordinate points are curve-fitted, and then the function expression of the curve is calculated as the acceleration function. Take the derivative of the coordinate point corresponding to the currently collected driving speed to obtain the acceleration of the current industrial vehicle.

[0095] S34: Determine whether the position information exceeds a preset position range. If so, issue a warning to pay attention to the driving direction. After successful identification of the driver's identity, according to the driver's operation plan, generate a corresponding driving route, and then determine the corresponding allowable driving position range. Continue to judge whether the position information returns within the position range within a preset time period t1. If it does not return, turn off the industrial vehicle and send a return warning to the management personnel. After receiving the warning, the management personnel can remotely control the industrial vehicle to make the vehicle automatically drive back to the starting point. Of course, the management personnel can also communicate with the driver to judge whether there is an abnormality in the preset position range, and then re-plan the driving route, modify the position range, and enable the driver to continue working.

[0096] S4: Monitor the parking of the industrial vehicle to make the industrial vehicle park in a preset parking area. When the driver performs the operation of turning off the engine, judge whether the current position information of the industrial vehicle is within the parking area. If so, perform face recognition on the driver. If the recognition is successful, confirm the end of the operation. Otherwise, issue an alarm for non-standard parking and lock the vehicle. When the driver readjusts the position of the industrial vehicle to meet the parking standard, the alarm is automatically cancelled. Perform face recognition on the driver. After successful recognition, unlock the industrial vehicle and confirm the end of the operation. After the driver leaves the cab, the industrial vehicle is automatically locked.

[0097] S5: Adopt a data processing method for industrial vehicle operations, and generate corresponding single-operation evaluations, industrial vehicle evaluations, and driver evaluations according to feature data, load data, driving data, and position information.

[0098] Please combine Figure 5 , which is Figure 1 The step diagram of the data processing method in. The data processing method includes data classification, data cleaning, and data derivation.

[0099] I. Data classification: Classify all the collected data according to the importance of the data. For example, measure each item of data from aspects such as safety, efficiency, cost, and failure, and then classify all the data into four categories: "very important", "important", "general", and "negligible". Among them, the importance ρ of each item of data i is expressed as:

[0100]

[0101] ω S + ω E + ω C + ω F = 1

[0102] In the formula, Score i (S) is the safety score, ω S is the weight of the safety score, Score i (E) is the efficiency score, ω E is the weight of the efficiency score, Score i (C) is the cost score, ω C is the weight of the cost score, Score i (F) is the failure score, ω F is the weight of the failure score.

[0103] When measuring the importance of data, it can be selected according to the specific application scenario and business requirements. For example, if the biggest concerns of an industrial vehicle company are safety and failure rate, then the importance of these data will be higher, and the weights of the corresponding indicators can also be set higher. However, all measurement indicators are within the scope of these indicators, only the weights can be different.

[0104] In this embodiment, the safety ratio ω S is set to 0.5, the efficiency ratio ω E is set to 0.2, the cost ratio ω C is set to 0.2, the failure ratio ω F is set to 0.1. The full score of each index score of each item of data is 10 points. Then if ρ i ≥ 0.8, it is "very important", if 0.6 ≤ ρ i < 0.8, it is "important", if 0.3 ≤ ρ i < 0.6, it is "general", if ρ i < 0.3, it is "negligible".

[0105] II. Data cleaning includes missing value processing, abnormal format processing, abnormal content processing, duplicate data processing and abnormal data processing.

[0106] Missing value processing is only applicable to data with missing values because missing values may occur in all data. The methods for processing missing values are as follows: ①. Directly remove the missing data with the category of "negligible" and mark the missing values. ②. Fill in the missing data with the category of "general" by fitting, and fill in the missing data with the category of "important" by prediction, and mark the filling respectively. Among them, for data without special requirements, linear fitting is generally used for fitting filling. ③. For the missing data with the category of "very important", data needs to be obtained again. If there are still missing values, a missing alarm is issued to remind the corresponding staff to conduct fault troubleshooting.

[0107] Abnormal format processing is used to mark the data whose format does not conform to a preset storage format, and then perform missing value processing according to the importance of the data. Each data has a corresponding data format during the process of transmission or storage. If the data does not conform to the preset data format when uploaded, it indicates that the data format is abnormal. At this time, it is still necessary to process according to the importance of the data, rather than obtaining all the data again.

[0108] Abnormal content processing is used to mark the data whose content exceeds a preset reasonable range, and then perform missing value processing according to the importance of the data. Each category of data has a corresponding data range when uploaded. If the data content significantly exceeds the reasonable range, it indicates that the data content is abnormal. The processing method for this data is the same as that for abnormal formats.

[0109] Duplicate data processing is used to calculate the hash value of each data and determine whether there are data with the same hash value within a time period. If so, keep one of the data and delete the other data with the same hash value. Duplicate data mainly targets feature data and location information because these two types of data are obtained based on time series. To reduce the pressure on data storage, each data is stored by calculating its hash value. Data with the same content corresponds to the same hash value, while data with the same hash value may have different contents. In this embodiment, only the data is compared with the data in the previous stage. If there are data with the same hash value, only one of the data needs to be kept and the other data is deleted.

[0110] Abnormal data processing is only applicable to feature data and location information, and is used to mark the data that is significantly abnormal in feature data and location information as abnormal data and issue an abnormal warning. For example, if a certain data has a large difference compared with the previous data and also has a large difference compared with the subsequent data (the time interval between the previous data and the subsequent data should not be too large), it can be determined as abnormal data. Mark this data and issue an abnormal warning to remind the staff to conduct fault troubleshooting.

[0111] The abnormal information of the cargo data and driving data can be checked manually, and the system can play a visual auxiliary role.

[0112] III. Data Derivation It is used to classify the cleaned data to form a driver dataset, an industrial vehicle dataset, and a single operation dataset, and then generate a single operation evaluation, an industrial vehicle evaluation, and a driver evaluation based on the data in each dataset.

[0113] Specifically, for the dataset of industrial vehicle operations, two classification labels should be established according to different drivers and different vehicles, namely the driver label and the industrial vehicle label. The two labels can be selected simultaneously or separately. According to the selected different labels, different data operation sets are filtered out, and then corresponding operations are performed on the filtered operation sets. Because, more than one driver will use the same vehicle, and at the same time, each driver will use more than one vehicle. By making horizontal comparisons between the same vehicle and different drivers, as well as between the same driver and different vehicles, and making vertical comparisons for the same vehicle or the same driver, data derivation can be carried out to obtain more useful information.

[0114] Among them, the single operation evaluation includes single path quality evaluation, single ideal operation energy consumption evaluation, and single operation energy consumption index evaluation. In the single path quality evaluation, if the actual driving distance is not much different from the ideal driving distance, there is no need to consider the driving path, and only the operation duration needs to be considered. If the actual driving distance is quite different from the ideal driving distance, both the driving path and the operation duration need to be considered, and the driving path is the main evaluation index, while the operation duration is the secondary evaluation index.

[0115] In this embodiment, the path matching degree will adopt the Fréchet distance. The Fréchet distance is an index used to measure the similarity between two paths. It is defined as the shortest distance between two paths, where one path moves along its parametric curve while the other path also moves along its parametric curve, minimizing the maximum Euclidean distance between the two curves.

[0116] Then the single path quality evaluation can be expressed as:

[0117] F(R r ,R i )=max(min(d(p1,q10,d(p2,q2),…,d(p k ,q k )))。i=1,2,3,……,k

[0118] In the formula, R r is the single driving path, p1,p2,…,p kis R r Some points of R i is the ideal driving path, q1, q2, …, q k are some points of R i R r and R i are both sets of coordinate points. d(p i , q i ) is the Euclidean distance between p i and q i . F(R r , R i ) is the Fréchet distance between two paths, representing the maximum matching degree of the two paths.

[0119] Let the single - trip driving distance be D r , the ideal driving distance be D i , the single - trip operation duration be T r , and the ideal operation duration be D i . Then the path matching index ρ m can be expressed as: If ρ m ≤0.05, it can be considered that the difference between the actual driving distance and the ideal driving distance is not large, and the operation duration index will be calculated, that is Then the single - trip path quality index can be expressed as:

[0120] ρ l =(1 - ρ m )*(1 - ρ t ).

[0121] If ρ m >0.05, it can be considered that the difference between the actual driving distance and the ideal driving distance is relatively large. At this time, the driving path is the main evaluation index, that is, the proportion weight of F(R r , R i ) is 0.6, and the proportion of the driving distance and the driving time are 0.2 respectively. Then the single - trip path quality index can be expressed as:

[0122] ρ l =F(R r , R i )*0.6+(1 - ρ m )*0.2+(1 - ρ t )*0.2.

[0123] The method for evaluating the energy consumption of a single ideal operation is as follows: Multiply the ideal driving distance and the load weight of this operation (the weight of the vehicle itself and the weight of the driver should also be considered during calculation) to obtain the work done, and then, according to the work done, calculate the ideal operation energy consumption of this time according to the ideal operation energy consumption fitting function F(W). The ideal operation energy consumption fitting function is obtained through previous tests and calculations on industrial vehicles of different models.

[0124] Then the work done W for a single operation is expressed as:

[0125] W = D i *(M g +M v +M d )

[0126] In the formula, D i is the ideal driving distance, M g is the load weight, M v is the weight of the vehicle itself, M d is the weight of the driver.

[0127] The corresponding ideal operation energy consumption for a single time can be calculated through the ideal operation energy consumption fitting function.

[0128] Then the evaluation of the energy consumption index ρ for a single operation e can be expressed as:

[0129]

[0130] In the formula, E r is the fuel consumption loss for a single operation, and E i is the ideal operation energy consumption for a single time.

[0131] The evaluation of industrial vehicles includes the evaluation of the vehicle engine loss index, the evaluation of the overall vehicle loss index, and the evaluation of the vehicle usage situation. Divide the sum of the energy consumption of a single operation of the industrial vehicle between two refuelings by the sum of the ideal operation energy consumption of a single time of the industrial vehicle between two refuelings, and the vehicle engine loss index can be obtained. Then the evaluation of the vehicle engine loss index ρ p is expressed as:

[0132]

[0133] In the formula, E ij is the sum of the ideal operation energy consumption of a single time of the industrial vehicle between two refuelings, Es is the total fuel consumption loss of the industrial vehicle between two refuelings, and n is the total number of the ideal operation energy consumption of a single time of the industrial vehicle between two refuelings.

[0134] When the industrial vehicle is an electric vehicle, the refueling volume can be converted into the charging volume.

[0135] The loss index of the vehicle engine (battery) is the initial overall vehicle loss index. There is an ideal value P for tire pressure i , and the tire pressure index is obtained by dividing the actual value by the ideal value. Assuming the number of tires is n v , then this index is the product of all tire indexes, and then multiplied by the initial value; the number of abnormal accessory states is m, and the initial value is multiplied by 0.9 m . If one of the abnormalities is repaired, one less 0.9 is multiplied. Let the number of accessory failures be M. If a failure occurs, the index is directly multiplied by 0.5 and drops below the qualified line (0.6). After the failure is repaired, the index is restored. Then the evaluation ρ of the overall vehicle loss index v is expressed as:

[0136]

[0137] In the formula, P i is the ideal value of tire pressure, P tj is the tire pressure, n v is the number of tires, m is the number of abnormal accessory states, and M is the number of accessory failures.

[0138] The evaluation of vehicle usage is divided into location distribution and time distribution. Location distribution: Through the vehicle location information, the activity area of the industrial vehicle within the selected time range is highlighted on the map. The more frequently the area is passed through, the darker the highlighted color. Time distribution: In the form of a histogram, the working time of the industrial vehicle within the selected time range is displayed (only the normal driving time periods are displayed. If the vehicle is only started but not driven, it is not included).

[0139] The driver evaluation includes the driver operation situation evaluation, the driver single - operation quality index, and the driver operation quality index.

[0140] The driver operation situation evaluation is similar to the vehicle usage evaluation. The difference is that the driver operation situation evaluation only shows the time distribution.

[0141] The initial operation quality index is obtained by multiplying the single - path quality index by the single - operation energy consumption index. The number of warnings is capped at three. If there are more than three warnings, the quality of this operation is unqualified. For each warning generated, the operation quality index is multiplied by 0.9 to obtain the final driver single - operation quality index. Then the driver single - operation quality index ρ d is expressed as:

[0142]

[0143] In the formula, ρ l is the single - path quality index, and n d is the number of warnings in the single - operation driving data.

[0144] The driver operation quality index is the average value of the single - operation quality indexes of the corresponding driver. Then the driver operation quality index ρ D is expressed as:

[0145]

[0146] In the formula, n D is the total number of operations.

[0147] For all the above "index" - type data, the closer the result is to 1, the higher the quality. All the above - mentioned derived data, as well as the normally obtained data, can be classified or grouped for viewing through tags, namely driver tags and vehicle tags.

[0148] The data - processing method of this embodiment classifies various data generated during the operation of industrial vehicles, deletes the data with lower importance, reduces the pressure of data storage and analysis. At the same time, by marking the missing or abnormally - content data, and then processing according to the importance of the corresponding data, such as fitting filling, predictive filling, re - obtaining, etc., and alarming when there is still abnormal data to remind the relevant operators to process, improving the effectiveness of the data, enhancing the rationality of data analysis, and further improving the supervision of industrial vehicle operations.

[0149] The management method of this embodiment verifies the identity of the driver to match the driver with the industrial vehicle, which can not only prevent the industrial vehicle from being randomly called, reducing the accident risk, but also facilitating the supervision of the driver's operation. Specifically, during the operation of the industrial vehicle, through the real - time supervision of the characteristic data and driving data, ensure that the fuel or power of the industrial vehicle is sufficient, and timely remind the driver when the industrial vehicle has a sudden failure for timely maintenance to ensure driving safety. At the same time, monitor the load data to prevent the goods carried during the operation from falling, exceeding the limit, or even damaging the industrial vehicle. By real - time monitoring of the position information of the industrial vehicle, the driver can always be on the predetermined operation route to assist the driver's operation, and at the same time, supervise the parking of the industrial vehicle to make the industrial vehicle parked in the preset position, avoiding problems such as large occupied area and inconvenient charging caused by random parking.

[0150] In the management of industrial vehicle operations, the above - mentioned management method is used to conduct real - time monitoring of the existing industrial vehicle management system, thereby improving the management efficiency and reducing the management cost. Please refer to Figure 6 , which is Figure 1 the structural schematic diagram of the management system adopting the management method of industrial vehicle operations in

[0151] Please combine with Figure 7 , which is Figure 6 a schematic diagram of the modular structure of an industrial vehicle in Each industrial vehicle includes a detection device (data measurement and control sub-module), an alarm, a display screen, and a controller (MCU processor module and edge computing module). The detection device is used to monitor in real time the characteristic data, load data, driving data, and position information of the industrial vehicle. The detection device may include multiple sensors and cameras. The sensors are respectively installed on various accessories of the industrial vehicle and are respectively used to detect the fuel quantity, power consumption, engine temperature, coolant temperature, tire pressure, circuit on / off state, load weight, load specifications (shape), vehicle speed, driving direction, front wheel angle, vehicle body deflection direction, and real-time position, etc. The cameras may be respectively installed on the four sides of the vehicle, inside the cab, etc., and are respectively used to collect road condition information, front photos of the driver, etc.

[0152] The alarm can be installed inside the cab and is used to give an audible and visual alarm in time when abnormal data appears to remind the driver to respond in time.

[0153] The display screen is used to display various data detected in real time, early warning information, identification interfaces, etc. For example, when the industrial vehicle is in operation, the vehicle speed, fuel quantity, position information, etc. can be displayed in real time to assist the driver in driving. At the same time, when abnormal data appears, the corresponding alarm information is displayed, which is convenient for the driver to timely discover the source of the abnormal data, and then conduct targeted troubleshooting and complete the maintenance as soon as possible. The display screen adopts a touch screen, which can not only be used as a tool for real-time display, but also can perform corresponding operations through the display screen, such as inputting an account number, querying various data, etc.

[0154] The controller includes an identification module, an edge computing module, an early warning signal generation module, and a control signal generation module.

[0155] The identification module includes an account identification module and a face recognition module. The account identification module is used to determine whether the user name and password input by the driver match. If the match is successful, the corresponding driver data is obtained; otherwise, an identification failure signal is generated. The driver data includes face photos, weight data, shape data, age, position, etc. Compare the user name and password input by the driver with the pre-stored accounts. If the match is successful, the corresponding driver data can be obtained, especially the corresponding face photos can be extracted for face recognition.

[0156] The face recognition module is used to determine whether the frontal photo of the driver collected in real time matches the corresponding face photo. If the match is successful, an unlocking signal is generated. Otherwise, a recognition failure signal is generated. In practical applications, a corresponding photo capture frame is displayed on the display screen. After the driver faces the camera, the driver adjusts the facial position so that the frontal photo is just within the photo capture frame. Within a time period (such as 10 s), multiple collected frontal photos are compared with the pre-stored face photos. If the recognition is successful, it indicates that the driver's identity is correct.

[0157] The edge computing module is used for: First, establish a three-dimensional coordinate system with the center of gravity of the industrial vehicle in the empty vehicle state as the origin, and calculate the center of gravity coordinates of the whole vehicle after loading the industrial vehicle in combination with the load data and the driver data. Specifically, map the industrial vehicle in a three-dimensional coordinate system according to the shape and weight of the industrial vehicle, calculate the center of gravity coordinates of the industrial vehicle, and move the origin of the three-dimensional coordinate system to the center of gravity coordinates of the industrial vehicle. Subsequently, map the driver into the three-dimensional coordinate system according to the driver's weight and the shape of the driver in the driving state, and map the carried goods into the same three-dimensional coordinate system according to the load weight and load specifications, and then calculate the center of gravity coordinates of the whole vehicle.

[0158] Second, select multiple driving speeds within the previous time period t2 from the driving data, and fit the multiple driving speeds to obtain an acceleration function. Take the derivative of the acceleration function to obtain the current acceleration of the industrial vehicle. For example, select multiple driving speeds within 5 s, and map the driving speeds in a plane coordinate system according to the corresponding collection times to obtain multiple coordinate points, and perform linear fitting on the multiple coordinate points to obtain the corresponding acceleration function. Furthermore, at the current driving speed, calculate the derivative of the corresponding coordinate point, that is, the current acceleration of the industrial vehicle. In other embodiments, multiple driving speeds can also be collected every other time period (such as 1 s), and then calculate the corresponding acceleration according to the ratio of the driving speed difference to the time. The difference is that the acceleration calculated by the ratio is the average acceleration within this time period.

[0159] The warning signal generation module is used for: First, generate a driver warning signal when the recognition module generates a recognition failure signal three times in a row. For example, if the driver enters incorrect account information three times in a row, or fails to recognize the face three times in a row, corresponding warning signals are generated. If the driver enters incorrect account information only once or twice in a row and then enters the correct account information, there are still three opportunities for face recognition.

[0160] II. When the characteristic data exceeds a preset index, a self-check warning signal is generated. The preset index can be stored in the controller or server in the form of a table. After the controller receives the detected characteristic data, it compares the characteristic data with the data in the table. If the characteristic data exceeds the allowable data range, corresponding warning signals are generated, such as warning signals for too low fuel level, too low tire pressure, too high engine temperature, etc.

[0161] III. When the center-of-gravity coordinates of the whole vehicle exceed a preset coordinate range, a load warning signal is generated. When the center-of-gravity coordinates of the whole vehicle exceed the preset coordinate range, it indicates that there are potential safety hazards in the industrial vehicle, and the carried goods should be adjusted in time. Specifically, the overall shape of the goods should be adjusted first, that is, the placement position of the goods is adjusted. If the center-of-gravity coordinates of the whole vehicle cannot be brought within the coordinate range only by adjusting the position, the weight of the goods should be reduced. During the adjustment process, it should also be noted that the carried goods shall not exceed the standard length, width, height, etc.

[0162] IV. When the driving speed exceeds a preset speed threshold or the acceleration exceeds a preset acceleration threshold, a deceleration warning signal is generated. Excessive driving speed poses a high safety risk, while excessive acceleration indicates improper operation by the driver. By timely reminding the driver to decelerate, the risk of accidents can be reduced.

[0163] V. When the position information exceeds a preset position range, a return warning signal is generated. During the operation process, the driver should drive along the planned route and shall not exceed the preset position range. Of course, if an accident occurs during the operation, such as too low fuel level, etc., the driver can apply to modify the driving route and reset the position range.

[0164] The control signal generation module is used to generate a vehicle locking signal when the warning signal generation module generates a driver warning signal, a self-check warning signal or a load warning signal. The vehicle locking signal is sent to the corresponding industrial vehicle, and the industrial vehicle is adjusted to the locked state and cannot be started. When a driver warning signal is generated, there is a risk of theft for the corresponding industrial vehicle. In addition to locking the vehicle in the off state, the doors should also be locked to further confirm the identity of the driver. When the self-check warning signal and the load warning signal are generated, only the off state of the vehicle should be locked and the doors should be opened to facilitate the driver to troubleshoot problems, adjust the position of the goods, etc.

[0165] The server is remotely connected to each industrial vehicle. The server is used for: I. Encoding each industrial vehicle. The encoding adopts two-level encoding. For example, the industrial vehicle is encoded at the first level, denoted as Ai, and the driver is encoded at the second level, denoted as Bi. Then when the driver performs operations, all types of detected data have the same encoding AiBi.

[0166] Second, interact with each industrial vehicle to receive and store feature data, load data, driving data, and location information. The server can establish corresponding storage data sets for each driver. For example, when establishing a data set B1 for driver B1, the server verifies the encoding of each piece of data during data reception, and all data encoded as A1B1, A2B1, A3B1, …… AiB1 are stored in the data set B1.

[0167] Third, adopt a data processing method for industrial vehicle operations. Based on the feature data, load data, driving data, and location information, generate corresponding single-operation evaluations, industrial vehicle evaluations, and driver evaluations.

[0168] The mobile terminal is remotely connected to the server. The mobile terminal is used to interact with the server, thereby realizing remote control of the industrial vehicle. In practical applications, the industrial vehicle is remotely connected to the base station through the gateway, and then realizes remote connection with the server through the base station, and can send various detected data to the server. The corresponding data is stored in the server, and the user can remotely connect to the server through the mobile terminal, thereby realizing data interaction with the server.

[0169] The mobile terminal can be a computer, a mobile phone, a tablet computer, etc., as long as it can realize remote connection with the server. The mobile terminal requests to query or download data of the corresponding driver or industrial vehicle from the server by logging in to the corresponding account or in the corresponding application program, facilitating real-time viewing of the operation status. At the same time, control instructions can be sent to the industrial vehicle through the server to realize remote control of the industrial vehicle.

[0170] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0171] The above-described embodiments only represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the appended claims.

Claims

1. A data processing method for industrial vehicle operations, which is used to process the characteristic data, load data, driving data, and location information generated during the operations of an industrial vehicle, and then respectively generate operation evaluations for the industrial vehicle and the driver, and optimize the management methods for the industrial vehicle and the driver according to the corresponding operation evaluations to improve the operation efficiency; characterized in that, The data processing method includes the following steps: I. Data Classification: All data are classified into four categories: "Very Important", "Important", "Average", and "Negligible" according to the importance of each piece of data. Among them, the importance ρ of each piece of data i is expressed as: ω S + ω E + ω C + ω F = 1 where Score i (S) is the safety score, ω S is the weight of the safety score, Score i (E) is the efficiency score, ω E is the weight of the efficiency score, Score i (C) is the cost score, ω C is the weight of the cost score, Score i (F) is the failure score, ω F is the weight of the failure score; Second, data cleaning, which includes missing value processing, abnormal format processing, abnormal content processing, duplicate data processing, and abnormal data processing; The missing value processing is only for the data with missing values; the specific method of missing value processing is as follows: ①. Directly remove the missing data with the category of "ignorable" and mark the missing values; ②. Fill in the missing data with the category of "general" by fitting, and fill in the missing data with the category of "important" by prediction, and mark the filling respectively; ③. For the missing data with the category of "very important", it is necessary to obtain the data again. If there are still missing values, a missing alarm is issued; The abnormal format processing is used to mark the data whose format does not conform to a preset storage format, and then perform missing value processing according to the importance of the data; The abnormal content processing is used to mark the data whose content exceeds a preset reasonable range, and then perform missing value processing according to the importance of the data; The duplicate data processing is used to calculate the hash value of each data and judge whether there are data with the same hash value within a time period. If so, keep one of the data and delete the other data with the same hash value; The abnormal data processing is used to mark the data that is obviously abnormal in the feature data and the location information as abnormal data and issue an abnormal warning; Third, data derivation, which is used to classify the cleaned data to form a driver dataset, an industrial vehicle dataset, and a single operation dataset, and then generate a single operation evaluation, an industrial vehicle evaluation, and a driver evaluation according to the data in each dataset.

2. The data processing method for industrial vehicle operation according to claim 1, characterized in that The single operation evaluation includes a single path quality evaluation and a single operation energy consumption index evaluation; the single path quality evaluation is expressed as: F(R r ,R i ) = max(min(d(p1,q1), d(p2,q2), …, d(p k ,q k ))); i = 1, 2, 3, ……, k Wherein, R r is the single-trip path, and p1, p2, …, p k are respectively the points on R r , R i is the ideal driving path, and q1, q2, …, q k are respectively the points on R i , d(p i , q i ) is the Euclidean distance between p i and q i , and F(R r , R i ) is the Fréchet distance between the two paths, representing the maximum matching degree of the two paths; The single-operation energy consumption index evaluation ρ e is expressed as: Where E r is the fuel loss per single operation, and E i is the ideal energy consumption per single operation.

3. The data processing method for industrial vehicle operations according to claim 1, wherein The industrial vehicle evaluation includes the evaluation of the vehicle engine loss index and the evaluation of the overall vehicle loss index; the evaluation of the vehicle engine loss index ρ p is expressed as: Wherein, E ij is the sum of the single ideal operation energy consumption of the industrial vehicle between two refuelings, and E s is the total fuel loss of the industrial vehicle between two refuelings, and n is the total number of the single ideal operation energy consumption of the industrial vehicle between two refuelings; The overall vehicle loss index evaluation ρ v is expressed as: Wherein, P i is the ideal value of the tire pressure, P tj is the tire pressure, n v is the number of tires, m is the number of abnormal accessory states, and M is the number of accessory failures.

4. The data processing method for industrial vehicle operation according to claim 1, wherein, The driver evaluation includes the single-operation quality index of the driver and the operation quality index of the driver; the single-operation quality index ρ of the driver d is expressed as: where ρ l is the single-path quality index, and n d is the number of warnings in the single-trip driving data; The driver operation quality index ρ D is expressed as: where n D is the total number of operations.

5. A management method for industrial vehicle operations, characterized in that, The management method includes the following steps: S1: Identify the driver. After successful identification, unlock the industrial vehicle; otherwise, lock the industrial vehicle; S2: After the industrial vehicle is started, perform a self-check on the industrial vehicle; the method of self-check is as follows: S21: Collect the feature data and load data of the industrial vehicle; S22: Judge whether the feature data and the load data exceed a preset index. If so, lock the industrial vehicle and issue an alarm; S23: Calculate the vehicle's center of gravity coordinates after loading according to the load data and the feature data; Judge whether the vehicle's center of gravity coordinates exceed a preset coordinate range. If so, lock the industrial vehicle and issue an alarm; S3: When the industrial vehicle is in a driving state, monitor the driving state, location information, and the feature data of the vehicle in real time to make the industrial vehicle drive safely within a preset moving range; the method of real-time monitoring of the driving state and the location information is as follows: S31: Collect the driving data and location information of the vehicle; the driving data includes the driving speed of the industrial vehicle; S32: Judge whether the driving speed is higher than a preset speed threshold. If so, issue a deceleration warning; S33: Calculate the current acceleration of the industrial vehicle based on the driving speed, and determine whether the acceleration exceeds a preset acceleration threshold. If so, issue a deceleration warning. S34: Determine whether the position information exceeds a preset position range. If so, issue a warning to pay attention to the driving direction. Continue to determine whether the position information returns to the position range within a preset time period t1. If it does not return, turn off the industrial vehicle and issue a return warning to the management personnel. S4: Monitor the parking of the industrial vehicle to enable the industrial vehicle to be parked in a preset parking area. S5: Adopt the data processing method for the operation of the industrial vehicle as described in any one of claims 1 to 4, and generate corresponding single-operation evaluations, industrial vehicle evaluations, and driver evaluations based on the characteristic data, load data, driving data, and position information.

6. The management method for industrial vehicle operations according to claim 5, characterized in that, In step S1, the driver's identity is identified by the following method: S11: The driver inputs a registered account number, and searches for the corresponding driver data in a pre-stored identity database; the driver data includes the account number and the driver's photo. S12: Collect the front photo of the driver, and determine whether the front photo matches the corresponding driver's photo through a pre-stored face recognition model. If so, it indicates successful identification.

7. The management method for industrial vehicle operations according to claim 5, characterized in that, In step S23, the calculation method of the vehicle's center-of-gravity coordinates is as follows: S231: Establish a three-dimensional coordinate system with the center of gravity of the industrial vehicle in the empty vehicle state as the origin. S232: Obtain the weight data of the driver, simulate the sitting posture of the driver in the driving state, and calculate the vehicle's center-of-gravity coordinates when the driver is in the driving state. S233: Map the load data to the three-dimensional coordinate system to obtain the load center of gravity, and then calculate the final overall center-of-gravity coordinates in combination with the vehicle's center of gravity.

8. The management method for industrial vehicle operations according to claim 5, characterized in that, In step S33, the calculation method of the acceleration is as follows: S331: Collect multiple recent driving speeds according to a preset time period t2. S332: Fit the multiple driving speeds to obtain an acceleration function, and then take the derivative of the acceleration function to obtain the current acceleration of the industrial vehicle.

9. A management system for industrial vehicle operations, which adopts the management method for industrial vehicle operations as described in any one of claims 5-8, characterized in that, The management system includes: Multiple industrial vehicles, and the industrial vehicles include detection devices; the detection devices are used to monitor the characteristic data, load data, driving data, and position information of the industrial vehicles in real time. A server, which is remotely connected to each industrial vehicle; the server is used for:

1. Encoding each industrial vehicle; 2. Conducting data interaction with each industrial vehicle, receiving and storing the characteristic data, load data, driving data, and position information; 3. Generating corresponding single-operation evaluations, industrial vehicle evaluations, and driver evaluations based on the characteristic data, load data, driving data, and position information of each industrial vehicle. A mobile terminal, which is remotely connected to the server; the mobile terminal is used to conduct data interaction with the server, and thus realize the remote control of the industrial vehicle.

10. The management system for industrial vehicle operations according to claim 9, characterized in that, The industrial vehicle further includes a controller, which includes an identification module, an edge computing module, a warning signal generation module, and a control signal generation module; The identification module includes an account identification module and a face recognition module; the account identification module is used to determine whether the user name and password entered by the driver match. If the match is successful, the corresponding driver data is obtained; otherwise, an identification failure signal is generated; the driver data includes a face photo and weight data; the face recognition module is used to determine whether the frontal photo of the driver collected in real time matches the corresponding face photo. If the match is successful, an unlocking signal is generated; otherwise, an identification failure signal is generated; The edge computing module is used for:

1. Establish a three-dimensional coordinate system with the center of gravity of the industrial vehicle in the empty vehicle state as the origin, and calculate the center of gravity coordinates of the whole vehicle after loading the industrial vehicle in combination with the load data and the driver data; 2. Select multiple driving speeds within the previous time period t2 from the driving data, fit the multiple driving speeds to obtain an acceleration function; and take the derivative of the acceleration function to obtain the current acceleration of the industrial vehicle; The warning signal generation module is used for:

1. Generate a driver warning signal when the identification module generates an identification failure signal three times in a row; 2. Generate a self-check warning signal when the characteristic data exceeds a preset index; 3. Generate a load warning signal when the center of gravity coordinates of the whole vehicle exceed a preset coordinate range; 4. Generate a deceleration warning signal when the driving speed exceeds a preset speed threshold or the acceleration exceeds a preset acceleration threshold; 5. Generate a return warning signal when the position information exceeds a preset position range; The control signal generation module is used to generate a vehicle locking signal when the warning signal generation module generates a driver warning signal, a self-check warning signal, or a load warning signal.

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