Residue soil truck transportation enterprise risk evaluation method

Abstract

The invention discloses a muck truck transportation enterprise risk evaluation method. The method comprises the steps of obtaining muck truck transportation enterprise information data, muck truck driver binding information data, driver information data, vehicle information data, illegal data and accident data; carrying out fusion processing on the obtained data, so that the driver, the vehicle, the enterprise, the illegal information and the accident information are in one-to-one correspondence; calculating the weight of each attribute; the slag car and driver risk score of each slag car transportation enterprise is calculated, and the final score of the slag car transportation enterprise is calculated; and determining high-risk, medium-risk and low-risk enterprises respectively by using a threshold method. According to the method, the risk of the muck truck transportation enterprise is judged through the comprehensive score, so that the related accident violation of the muck truck is greatly reduced, and the road safety order is enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of muck truck transportation, and in particular relates to a risk assessment method for a muck truck transportation enterprise. Background Art

[0002] At present, the muck truck transportation industry generally has problems such as poor traffic law awareness among drivers, many hidden dangers in vehicle safety performance, inadequate safety management, prominent traffic violations, and most of the travel time is concentrated at night, making it difficult for police to enforce the law. Therefore, a reasonable risk assessment method for muck truck transportation companies is urgently needed to improve relevant supervision.

[0003] Existing methods for evaluating the risks of transportation companies mainly rely on publicly available or desensitized data on the Internet to collect relevant information about drivers and vehicles. The data information obtained by this method is not deep enough and key details are missing, resulting in a one-sided assessment of driver behavior and vehicle condition analysis, which in turn leads to inaccurate assessment of corporate risks. Summary of the invention

[0004] In view of the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a risk assessment method for muck truck transportation companies. The present invention obtains relevant vehicle information of muck truck transportation companies, relevant operation and management records of transportation companies, and relevant accident violation data of drivers of muck truck transportation companies, and calculates the indicators through hierarchical analysis method to obtain the comprehensive score of each muck truck transportation company. The risk of muck truck transportation companies is determined by the comprehensive score, which greatly reduces accident violations related to muck trucks and strengthens road safety order.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A risk assessment method for a muck truck transportation enterprise of the present invention comprises the following steps:

[0007] 1) Obtain information data of muck truck transportation companies through traffic management big data, including vehicle license plate type, vehicle license plate number, company name, and company number;

[0008] 2) Obtain the binding information data of the muck truck driver through traffic control big data, including the driver's name, driver's license number, license plate number, and license plate type;

[0009] 3) Obtain driver information data through traffic control big data, including driver's name, driver's license number, driver's age, driver's first license date, driver's permitted vehicle type, driver's license status, and points;

[0010] 4) Obtain vehicle information data through traffic control big data, including vehicle license plate type, vehicle license plate number, initial registration date, last inspection date, next inspection date, and vehicle status;

[0011] 5) Obtain violation data through traffic control big data, including the type of illegal vehicle license plate, the illegal vehicle license plate number, the person involved in the illegal vehicle, the driver's license number of the person involved in the illegal vehicle, the illegal behavior, the illegal time, and the illegal score;

[0012] 6) Obtain accident data through traffic control big data, including the type of accident vehicle license plate, the accident vehicle license plate number, the name of the accident vehicle driver, the accident vehicle driver identification number, accident responsibility, accident event, accident cause, death toll, injury toll, and property loss;

[0013] 7) Fusing the data obtained in step 1) to step 6) to make the driver, vehicle, enterprise, violation, and accident information correspond one to one;

[0014] 8) Calculate the weight of each attribute according to the entropy method;

[0015] 9) Calculating the risk scores of the slag trucks and drivers of each slag truck transportation enterprise according to the weights obtained in step 8), and calculating the final score of the slag truck transportation enterprise;

[0016] 10) Based on the final scores obtained in step 9), use the threshold method to determine high, medium and low risk enterprises respectively.

[0017] Furthermore, the data processing in step 7) specifically includes:

[0018] 71) Binding the information of the muck truck transportation company and the muck truck driver to the transportation company through the vehicle license plate number and vehicle license plate type;

[0019] 72) One-to-many binding of vehicle license plate numbers, vehicle license plate types, and accident and violation data;

[0020] 73) Bind the driver's name, ID number / driver's license number to accidents and violations in a one-to-many manner.

[0021] Furthermore, the specific steps of step 8) are as follows:

[0022] 81) Construct a decision matrix, where each column represents the attributes of the decision and each row represents a muck truck / driver;

[0023] 82) The decision matrix is ​​normalized so that the value of each attribute is in the range of [0,1]. The calculation formula is as follows:

[0024]

[0025] Where X represents the decision matrix, X unifyrepresents the normalized matrix, X min and X max Represent the minimum and maximum values ​​in the decision matrix respectively;

[0026] 83) Calculate the information entropy of each attribute using the following formula:

[0027]

[0028] Among them, H i represents the information entropy of attribute i, x ij represents the normalized value of the muck truck / driver on attribute i, p ij It represents the characteristic weight of the i-th evaluation object under the j-th indicator. The indicator refers to the attributes of a column of decision-making, and the evaluation object refers to the muck truck / driver;

[0029] 84) Calculate the weight of each attribute using the following formula:

[0030]

[0031] Among them, W i represents the weight of attribute i, n represents the number of attributes, 1-E i represents the coefficient of variation;

[0032] 85) The calculated weights are normalized so that the sum of all attribute weights is 1.

[0033] Furthermore, the specific steps of step 9) are as follows:

[0034] 91) The scores of each attribute are weighted and summed to obtain the score of the muck truck transportation enterprise. The calculation formula is as follows:

[0035]

[0036] Among them, c j represents the score of the jth muck truck / driver, s i represents the score of attribute i;

[0037] 92) Perform min-max normalization on the muck truck / driver score to obtain the muck truck / driver normalized score, and the calculation formula is as follows:

[0038]

[0039] Among them, c max 、c min Respectively represent the maximum and minimum scores of the muck truck / driver, S j represents the normalized score of the jth muck truck / driver;

[0040] 93) According to the binding relationship in step 7), the final score of the muck truck transportation company is calculated, and the calculation formula is as follows:

[0041]

[0042] Among them, S n is the final score of the nth muck truck transportation company, S jn It is the normalized score of the muck truck / driver bound to the nth muck truck transportation company.

[0043] Furthermore, the step 10) specifically includes:

[0044] 101) According to the final score of the muck truck transportation enterprise obtained in step 9), set high, medium and low thresholds as follows:

[0045] Calculate the mean μ and standard deviation σ of the final scores of muck truck transportation companies;

[0046]

[0047] Among them, n is the number of muck truck transportation companies, S n is the final score of the nth muck truck transportation company;

[0048] Set the thresholds to μ+σ, μ-σ respectively;

[0049] 102) Enterprises are classified into high, medium and low risk enterprises as follows:

[0050] When S n When <μ-σ, the risk is low;

[0051] When μ-σ≤S n When ≤μ+σ, the risk is medium;

[0052] When S n When >μ+σ, the risk is high.

[0053] Beneficial effects of the present invention:

[0054] The present invention obtains the specific illegal and accident behaviors of vehicles and drivers related to the muck truck transportation enterprise from the traffic management big data, and can calculate the weights of various data. The weights calculated by the entropy method are more objective than the existing technology, and can reduce the result deviation caused by subjective factors. It can better evaluate the risks of muck truck transportation enterprises than the existing technology, and the evaluation results are better.

[0055] The present invention standardizes the calculated results, calculates the mean and variance of each final score, and uses the threshold method to set the risk level, which is more convincing. At the same time, the threshold can be further adjusted according to practice, such as μ-2σ, μ+2σ, etc., to make the threshold more flexible and in line with actual application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0057] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0058] Reference Figure 1 As shown, a risk assessment method for a muck truck transportation enterprise of the present invention comprises the following steps:

[0059] 1) Obtain information data of muck truck transportation companies through traffic management big data, including vehicle license plate type, vehicle license plate number, company name, and company number;

[0060] 2) Obtain the binding information data of the muck truck driver through traffic control big data, including the driver's name, driver's license number, license plate number, and license plate type;

[0061] 3) Obtain driver information data through traffic control big data, including driver's name, driver's license number, driver's age, driver's first license date, driver's permitted vehicle type, driver's license status, and points;

[0062] 4) Obtain vehicle information data through traffic control big data, including vehicle license plate type, vehicle license plate number, initial registration date, last inspection date, next inspection date, and vehicle status;

[0063] 5) Obtain violation data through traffic control big data, including the type of illegal vehicle license plate, the illegal vehicle license plate number, the person involved in the illegal vehicle, the driver's license number of the person involved in the illegal vehicle, the illegal behavior, the illegal time, and the illegal score;

[0064] 6) Obtain accident data through traffic control big data, including the type of accident vehicle license plate, the accident vehicle license plate number, the name of the accident vehicle driver, the accident vehicle driver identification number, accident responsibility, accident event, accident cause, death toll, injury toll, and property loss;

[0065] 7) The data obtained in step 1) to step 6) are integrated and processed to make the driver, vehicle, enterprise, violation and accident information correspond to each other; specifically including:

[0066] 71) Binding the information of the muck truck transportation company and the muck truck driver to the transportation company through the vehicle license plate number and vehicle license plate type;

[0067] 72) One-to-many binding of vehicle license plate numbers, vehicle license plate types, and accident and violation data;

[0068] 73) Bind the driver's name, ID number / driver's license number to accidents and violations in a one-to-many manner.

[0069] 8) Calculate the weight of each attribute according to the entropy method; specifically including:

[0070] 81) Construct a decision matrix, where each column represents the attributes of the decision and each row represents a muck truck / driver;

[0071] 82) The decision matrix is ​​normalized so that the value of each attribute is in the range of [0,1]. The calculation formula is as follows:

[0072]

[0073] Where X represents the decision matrix, X unify represents the normalized matrix, X min and X max Represent the minimum and maximum values ​​in the decision matrix respectively;

[0074] 83) Calculate the information entropy of each attribute using the following formula:

[0075]

[0076] Among them, H i represents the information entropy of attribute i, x ij represents the normalized value of the muck truck / driver on attribute i, p ij It represents the characteristic weight of the i-th evaluation object under the j-th indicator. The indicator refers to the attributes of a column of decision-making, and the evaluation object refers to the muck truck / driver;

[0077] 84) Calculate the weight of each attribute using the following formula:

[0078]

[0079] Among them, W i represents the weight of attribute i, n represents the number of attributes, 1-E i represents the coefficient of variation;

[0080] 85) The calculated weights are normalized so that the sum of all attribute weights is 1.

[0081] 9) Calculate the risk scores of the slag trucks and drivers of each slag truck transportation enterprise according to the weights obtained in step 8), and calculate the final score of the slag truck transportation enterprise; specifically including:

[0082] 91) The scores of each attribute are weighted and summed to obtain the score of the muck truck transportation enterprise. The calculation formula is as follows:

[0083]

[0084] Among them, c j represents the score of the jth muck truck / driver, s i represents the score of attribute i;

[0085] 92) Perform min-max normalization on the muck truck / driver score to obtain the muck truck / driver normalized score, and the calculation formula is as follows:

[0086]

[0087] Among them, c max 、c min Respectively represent the maximum and minimum scores of the muck truck / driver, S j represents the normalized score of the jth muck truck / driver;

[0088] 93) According to the binding relationship in step 7), the final score of the muck truck transportation company is calculated, and the calculation formula is as follows:

[0089]

[0090] Among them, S n is the final score of the nth muck truck transportation company, S jn It is the normalized score of the muck truck / driver bound to the nth muck truck transportation company.

[0091] 10) Based on the final scores obtained in step 9), use the threshold method to determine high, medium and low risk enterprises respectively; specifically, including:

[0092] 101) According to the final score of the muck truck transportation enterprise obtained in step 9), set high, medium and low thresholds as follows:

[0093] Calculate the mean μ and standard deviation σ of the final scores of muck truck transportation companies;

[0094]

[0095] Among them, n is the number of muck truck transportation companies, S n is the final score of the nth muck truck transportation company;

[0096] Set the thresholds to μ+σ, μ-σ respectively;

[0097] 102) Enterprises are classified into high, medium and low risk enterprises as follows:

[0098] When S n When <μ-σ, the risk is low;

[0099] When μ-σ≤S n When ≤μ+σ, the risk is medium;

[0100] When S n When >μ+σ, the risk is high.

[0101] The present invention has many specific application paths. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements can be made without departing from the principle of the present invention. These improvements should also be regarded as the protection scope of the present invention.

Claims

1. A risk assessment method for muck truck transportation enterprises, characterized in that: Here are the steps: 1) Obtain information data of muck truck transportation companies, including vehicle license plate type, vehicle license plate number, company name and company number; 2) Obtain the binding information data of the muck truck driver, including the driver's name, driver's license number, license plate number and license plate type; 3) Obtain driver information data, including driver's name, driver's license number, driver's age, driver's first license date, driver's permitted vehicle type, driver's license status and points; 4) Obtain vehicle information data, including vehicle license plate type, vehicle license plate number, initial registration date, last inspection date, next inspection date and vehicle status; 5) Obtaining violation data, including the type of illegal vehicle license plate, the illegal vehicle license plate number, the person involved in the illegal vehicle, the driver's license number of the person involved in the illegal vehicle, the illegal behavior, the time of the violation and the number of illegal penalties; 6) Obtain accident data, including the type of accident vehicle license plate, the accident vehicle license plate number, the name of the accident vehicle driver, the accident vehicle driver identification number, accident responsibility, accident event, accident cause, death toll, injury toll and property loss; 7) Fusing the data obtained in step 1) to step 6) to make the driver, vehicle, enterprise, violation, and accident information correspond one to one; 8) Calculate the weight of each attribute; 9) Calculating the risk scores of the slag trucks and drivers of each slag truck transportation enterprise according to the weights obtained in step 8), and calculating the final score of the slag truck transportation enterprise; 10) Based on the final scores obtained in step 9), use the threshold method to determine high, medium and low risk enterprises respectively.

2. The risk assessment method for muck truck transportation enterprises according to claim 1 is characterized in that: The data processing in step 7) specifically includes: 71) Binding the information of the muck truck transportation company and the muck truck driver to the transportation company through the vehicle license plate number and vehicle license plate type; 72) One-to-many binding of vehicle license plate numbers, vehicle license plate types, and accident and violation data; 73) Bind the driver's name, ID number / driver's license number to accidents and violations in a one-to-many manner.

3. The risk assessment method for muck truck transportation enterprises according to claim 1 is characterized in that: The specific steps of step 8) are as follows: 81) Construct a decision matrix, where each column represents the attributes of the decision and each row represents a muck truck / driver; 82) The decision matrix is ​​normalized so that the value of each attribute is in the range of [0,1]. The calculation formula is as follows: Where X represents the decision matrix, X unify represents the normalized matrix, X min and X max Represent the minimum and maximum values ​​in the decision matrix respectively; 83) Calculate the information entropy of each attribute using the following formula: Among them, H i represents the information entropy of attribute i, x ij represents the normalized value of the muck truck / driver on attribute i, p ij It represents the characteristic weight of the i-th evaluation object under the j-th indicator. The indicator refers to the attributes of a column of decision-making, and the evaluation object refers to the muck truck / driver; 84) Calculate the weight of each attribute using the following formula: Among them, W i represents the weight of attribute i, n represents the number of attributes, 1-E i represents the coefficient of variation; 85) The calculated weights are normalized so that the sum of all attribute weights is 1.

4. The risk assessment method for muck truck transportation enterprises according to claim 3 is characterized in that: The specific steps of step 9) are as follows: 91) The scores of each attribute are weighted and summed to obtain the score of the muck truck transportation enterprise. The calculation formula is as follows: Among them, c j represents the score of the jth muck truck / driver, s i represents the score of attribute i; 92) Perform min-max normalization on the muck truck / driver score to obtain the muck truck / driver normalized score, and the calculation formula is as follows: Among them, c max 、c min Respectively represent the maximum and minimum scores of the muck truck / driver, S j represents the normalized score of the jth muck truck / driver; 93) According to the binding relationship in step 7), the final score of the muck truck transportation company is calculated as follows: Among them, S n is the final score of the nth muck truck transportation company, S jn It is the normalized score of the muck truck / driver bound to the nth muck truck transportation company.

5. The risk assessment method for muck truck transportation enterprises according to claim 4 is characterized in that: The step 10) specifically includes: 101) According to the final score of the muck truck transportation enterprise obtained in step 9), set high, medium and low thresholds as follows: Calculate the mean μ and standard deviation σ of the final scores of muck truck transportation companies; Among them, n is the number of muck truck transportation companies, S n is the final score of the nth muck truck transportation company; Set the thresholds to μ+σ, μ-σ respectively; 102) Enterprises are classified into high, medium and low risk enterprises as follows: When S n When <μ-σ, the risk is low; When μ-σ≤S n When ≤μ+σ, the risk is medium; When S n When >μ+σ, the risk is high.

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