Method and system for evaluating treatment efficiency of electric bicycle

Abstract

The invention discloses a method and system for evaluating the governance efficiency of an electric bicycle, and belongs to the field of electric bicycle governance, and the method comprises the following steps: extracting the illegal and accident data of the electric bicycle; analyzing the illegal and accident data of the electric bicycle based on a logic analysis method to obtain various causes, and constructing an evaluation factor matrix; carrying out extraction calculation on various causes by adopting a rank sum ratio method to obtain the weight of each cause; and matching the weight of each cause with the evaluation factor matrix based on a regression tree algorithm to obtain the specific cause management efficiency of the electric bicycle.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric bicycle management, and in particular relates to a method and system for evaluating the management efficiency of an electric bicycle. Background Art

[0002] Electric bicycles are a convenient and flexible way of traveling short distances that are very popular among people. They are an important part of urban transportation, but they are also a mode of transportation with high safety risks. Due to problems such as vehicle conditions, road environment, traffic organization mode and rider behavior habits, they pose severe challenges to urban traffic management. At the same time, practical problems such as the large number of electric bicycle riders and the difficulty of law enforcement and control are plaguing front-line traffic police. At the same time, there are differences in the evaluation standards for the effectiveness of electric bicycle management by traffic management departments in various places, and the content is relatively broad. Strengthening the comprehensive management, source management, and linkage management of electric bicycles has become a social development need. To this end, the present invention proposes an evaluation method and system for electric bicycle management based on the rank sum ratio method RSR, which is convenient for scientifically and objectively evaluating the effectiveness of electric bicycle management and reflecting the effectiveness of electric bicycle management. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method and system for evaluating the management efficiency of electric bicycles to solve the problems existing in the above prior art.

[0004] To achieve the above object, the present invention provides a method for evaluating the efficiency of electric bicycle management, comprising the following steps:

[0005] Extracting electric bicycle violation and accident data;

[0006] Based on the logic analysis method, the electric bicycle violation and accident data are analyzed to obtain various causes, and an evaluation factor matrix is ​​constructed;

[0007] The rank sum ratio method is used to extract and calculate various causes and obtain the weight of each cause;

[0008] Based on the regression tree algorithm, the weight of each cause and the evaluation factor matrix are matched to obtain the specific cause effectiveness of electric bicycle management.

[0009] Optionally, the evaluation factor matrix includes: a law and policy category set, an order management category set, a vehicle condition category set, a road facility category set, a safety protection category set and a publicity and education category set.

[0010] Optionally, the set of regulations and policies includes: national laws, local legislation, regulations and guidelines, rules and regulations, policy normative documents, notices and notifications, and standard guidelines;

[0011] The order management category set includes: road rights, vehicle attributes, restricted passage, prohibited passage, speed limit reminder, traffic organization, driving against traffic, running a red light, illegal occupation of the road, speeding, carrying people, carrying goods, and distracted driving;

[0012] The vehicle condition category set includes: body structure, maximum design speed, vehicle mass, battery, CCC certification, electronic identification, short circuit protection, leakage protection, GPS positioning, and ABS;

[0013] The road facility category set includes: traffic signs, traffic markings, non-motorized vehicle lanes, traffic lights, isolation guardrails, stabilization facilities, vehicle blocking piles, vibrating markings, reflective road studs, and non-motorized vehicle signal lights;

[0014] The safety protection category set includes: safety helmet, child safety seat, reflective vest, goggles, and pedestrian avoidance;

[0015] The publicity and education category set includes: compliance with traffic rules, cycling safety awareness, civilized cycling, warning education, children's safety, green travel, elderly safety, and slowing down.

[0016] Optionally, before extracting and calculating the causes using the rank sum ratio method, the step further includes ranking the evaluation object of each evaluation factor;

[0017] Among them, electric bicycles are ranked in ascending order according to the severity of violations and accidents; electric bicycles are ranked in descending order according to the management strategies and means; and when the values ​​of several evaluation objects on specific evaluation factors are exactly the same, an average ranking is performed.

[0018] Optionally, the process of extracting and calculating the cause using the rank sum ratio method includes:

[0019] Calculate the rank sum ratio of the evaluation object based on the ranked evaluation object;

[0020] The rank sum ratio method was used to calculate the cumulative frequency of each cause;

[0021] The linear regression equation was calculated based on the cumulative frequency and rank sum ratio of each cause;

[0022] The weight of each cause is calculated based on the linear regression equation.

[0023] Optionally, the method for evaluating the effectiveness of electric bicycle management also includes calculating the overall effectiveness of electric bicycle management based on the weights of various causes and extracting the total amount of electric bicycle violations and accident data;

[0024] Among them, the formula for calculating the overall effectiveness of electric bicycle management is:

[0025]

[0026] In the formula, τ represents the overall effectiveness of electric bicycle management, ε represents the calculated value of the regression equation, and γ represents the total amount of electric bicycle violation and accident data extracted.

[0027] Optionally, the process of calculating the specific cause effectiveness of electric bicycle governance includes:

[0028] Calculate the average cause weight value based on the weight of each cause;

[0029] Calculate the evaluation factor benchmarks of various causes based on the average cause weight value;

[0030] Calculate the average violation and accident value based on the total amount of electric bicycle violation and accident data extracted;

[0031] Calculate the assessment level of each cause based on the average violation and incident values;

[0032] The evaluation factor benchmarks and evaluation levels of various factors are compared respectively to obtain the specific causal effectiveness of electric bicycle management.

[0033] The present invention also provides a system for evaluating the effectiveness of electric bicycle management, including: a data extraction module, a cause analysis module, a factor classification module, an evaluation factor matrix construction module, a ranking module, an RSR calculation module and a distribution determination module;

[0034] Wherein, the data extraction module is used to extract electric bicycle violation and accident data from the public security traffic management database;

[0035] The cause analysis module is used to analyze the causes of electric bicycle violations and accidents using logic analysis;

[0036] The factor classification module is used to classify the causes into six categories: laws and policies, order management, vehicle conditions, road facilities, safety protection and publicity and education;

[0037] The evaluation factor matrix construction module is used to construct an evaluation factor matrix;

[0038] The ranking module is used to rank the evaluation object of each evaluation factor;

[0039] The RSR calculation module is used to calculate the rank sum ratio and the weighted rank sum ratio;

[0040] The governance effectiveness determination module determines the weight of each cause based on the rank sum ratio and matches the weight of each cause with the evaluation factor matrix to obtain the specific cause effectiveness of electric bicycle governance.

[0041] Compared with the prior art, the present invention has the following advantages and technical effects:

[0042] The present invention can objectively and quantitatively judge the effectiveness of electric bicycle law enforcement, provide early warning of potential risks of electric bicycles, and can intuitively, efficiently and quickly respond to the shortcomings and weaknesses of current electric bicycle management, which is conducive to discovering problems and correcting them in a timely manner, and promoting the improvement of the comprehensive management efficiency of electric bicycles. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1 The figure is a flow chart of an evaluation method for electric bicycle management based on the rank sum ratio method (RSR) according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0047] Embodiment 1

[0048] like Figure 1 As shown, a method for evaluating the effectiveness of electric bicycle management is provided in this embodiment. First, the electric bicycle violation and accident data are extracted based on the public security traffic management database. Secondly, the causes of electric bicycle violations and accidents are analyzed using the logical analysis method, and the causes are extracted and calculated using the rank sum ratio method RSR to obtain high-dimensional semantic features. Finally, the regression tree algorithm is used to match the extracted semantics with the existing evaluation factors, and the evaluation factor category set is divided. As a specific implementation method of this embodiment, the following steps are included: extracting electric bicycle violation and accident data; analyzing the electric bicycle violation and accident data based on the logical analysis method to obtain various causes, and constructing an evaluation factor matrix; extracting and calculating various causes using the rank sum ratio method to obtain the weight of each cause; matching the weight of each cause with the evaluation factor matrix based on the regression tree algorithm to obtain the specific cause effectiveness of electric bicycle management.

[0049] Furthermore, the specific implementation of this embodiment includes:

[0050] 1. Analyze and sort the original data of electric bicycle violations and accidents

[0051] Arrange the m evaluation indicators of n evaluation objects into an original data set with n rows and m columns. Compile the ranking of each evaluation object for each indicator, among which the severity of electric bicycle violations and accidents is ranked in ascending order, and the management strategies and means of electric bicycles in various places are ranked in descending order. The average rank is compiled for the same indicator data.

[0052] 2. Calculate the rank sum ratio (RSR) / weighted rank sum ratio and sort directly

[0053] The rank sum ratio is calculated according to the formula. When the weights of each evaluation index are different, the weighted rank sum ratio (WRSR) is calculated.

[0054]

[0055] The evaluation objects are comprehensively ranked by the size of the rank sum ratio (RSR) value. Since there are many evaluation objects in this case, they need to be classified, so the distribution of RSR should be found first.

[0056] 3. Determine the distribution of RSR (calculate probability units)

[0057] The purpose of determining the RSR distribution is to express the cumulative frequency of a specific value using the probability unit Probit. The steps are:

[0058] ① Prepare the RSR frequency distribution table, list the frequency f of each group, and calculate the cumulative frequency ∑f of each group;

[0059] ② Determine the rank range R and average rank of each group of RSR;

[0060] ③Calculate the cumulative frequency, and finally correct the accumulation according to (1-1 / kn);

[0061] ④ Convert the percentage P into the probability unit Probit. Probit is the standard normal deviation corresponding to the percentage P.

[0062] 4. Calculate the linear regression equation

[0063] The probability unit Probit corresponding to the cumulative frequency is used as the independent variable, and the RSR (or WRSR) value is used as the dependent variable to calculate the linear regression equation. The evaluation objects are ranked according to the RSR (WRSR) estimated value corresponding to the regression equation.

[0064] This application conducts a causal analysis on electric bicycle violations and accident data, and uses cluster analysis to divide the causes of electric bicycle violations and accidents into six dimensional factors: laws and regulations, order management, vehicle conditions, publicity and education, road facilities, and safety protection. The six evaluation factors are graded and weighted according to different governance needs, and the level of electric bicycle governance is intuitively reflected through calculations.

[0065] Furthermore, the process of extracting and calculating the causes using the rank sum ratio method includes: calculating the rank sum ratio of the evaluation object based on the ranked evaluation object; measuring the cumulative frequency of each type of cause using the rank sum ratio method; calculating the linear regression equation based on the cumulative frequency and rank sum ratio of each type of cause; and calculating the weight of each type of cause based on the linear regression equation.

[0066] The rank sum ratio method (RSR) is used to analyze the causes of electric bicycle violations and accidents. The total amount of electric bicycle violations and accidents data extracted is γ, the amount of laws and policies is α, the amount of order management is β, and the amount of vehicle conditions is The contributing factor of road facilities is ρ, the contributing factor of safety protection is σ, and the contributing factor of publicity and education is μ. The rank sum ratio method (RSR) is used to calculate the cumulative frequency of each category of causes through RSR frequency. The weight of each category of causes is calculated according to the regression equation, and the results are: laws and regulations 1.2, order management 1.1, vehicle conditions 0.8, road facilities 0.8, safety protection 0.6, and publicity and education 0.5.

[0067] ①

[0068]

[0069] ②

[0070]

[0071] ③

[0072]

[0073] The larger the τ value, the better the overall effectiveness of the city's electric bicycle management, and the fewer aspects that need to be improved and perfected; when the τ value is smaller, it means that the overall effect of the city's electric bicycle management is not ideal, and there are more aspects that need to be focused on and improved, and the research and judgment of measures to improve the level of electric bicycle traffic management should be strengthened.

[0074] Furthermore, the process of calculating the effectiveness of specific causes of electric bicycle management includes: calculating the average cause weight value based on the weight of each cause; calculating the evaluation factor benchmark of each cause based on the average cause weight value; calculating the average violation and accident value based on the total amount of electric bicycle violation and accident data extracted; calculating the evaluation level of each cause based on the average violation and accident value.

[0075] Compare the evaluation factor benchmarks and evaluation levels of various factors to obtain the specific cause effectiveness of electric bicycle management

[0076] In order to deeply explore the specific weaknesses and shortcomings of the six categories of electric bicycle management, the following comparative analysis is performed:

[0077] ④

[0078]

[0079]

[0080] L 1 : represents the benchmark for regulatory policy assessment factors; L 2 : represents the order management evaluation factor benchmark; L 3 : represents the vehicle condition assessment factor benchmark; L 4 : represents the road facility assessment factor benchmark; L 5 : represents the safety protection assessment factor benchmark; L 6 : Represents the publicity and education evaluation factor benchmark.

[0081] ⑤

[0082]

[0083] ω 1 Represents the actual measurement of regulatory policy evaluation level; 2 Represents the actual measured order management assessment level; ω 3 Represents the actual measured vehicle condition assessment level; ω 4 Represents the actual measured road facility assessment level; ω 5 Represents the actual measured safety protection assessment level; ω 6 Represents the actual measurement of publicity and education evaluation level.

[0084] When 1 <L 1 This indicates that the area needs to strengthen the formulation and revision of relevant laws and regulations on electric bicycles and pay attention to the improvement of regulations and standards. 1 ≥L 1 When , it means that the current laws and policies on electric bicycle management in the area are relatively complete, and the public feels good about it; when ω 2 <L 2 This indicates that the area needs to strengthen the management of electric bicycle order, and strong law enforcement can regulate riders to abide by traffic rules; when 2 ≥L 2 It shows that the current control and investigation of the traffic order of electric bicycles is relatively strong and has a strong deterrent effect; when 3 <L 3 When ω 3 ≥L 3 This indicates that the current status of electric bicycles needs more attention, and manufacturers and the market need to increase supervision and constraints; when 4 <L 4This indicates that the road facilities for electric bicycles need to be improved. 4 ≥L 4 When , it means that more attention is paid to the construction of infrastructure for electric bicycle road traffic; when ω 5 <L 5 This indicates that the current protective measures for electric bicycle riders, such as safety helmets and child seats, are not in place. 5 ≥L 5 When , it means that the current electric bicycle riders actively wear safety helmets, child seats and other protective measures; when ω 6 <L 6 This indicates that the current publicity and guidance work on safe and civilized riding of electric bicycles is not in place. 6 ≥L 6 This shows that the current publicity and education work on safe and civilized riding of electric bicycles in this area is relatively good.

[0085] Embodiment 2

[0086] This embodiment provides a system for evaluating the effectiveness of electric bicycle management, including: a data extraction module, a cause analysis module, a factor classification module, an evaluation factor matrix construction module, a ranking module, an RSR calculation module and a distribution determination module;

[0087] Among them, the data extraction module is used to extract electric bicycle violation and accident data from the public security traffic management database;

[0088] The cause analysis module is used to analyze the causes of electric bicycle violations and accidents using logical analysis methods;

[0089] The factor classification module is used to classify the causes into six categories: laws and policies, order management, vehicle conditions, road facilities, safety protection, and publicity and education;

[0090] The evaluation factor matrix building module is used to build the evaluation factor matrix;

[0091] The ranking module is used to rank the evaluation object of each evaluation factor;

[0092] The RSR calculation module is used to calculate the rank sum ratio and the weighted rank sum ratio;

[0093] The governance effectiveness determination module determines the weight of each cause based on the rank sum ratio and matches the weight of each cause with the evaluation factor matrix to obtain the specific cause effectiveness of electric bicycle governance.

[0094] Furthermore, the system analyzes the causes of electric bicycle violations and accidents, extracts and splits them into six categories of electric bicycle governance assessment factors, including laws and regulations, order management, vehicle conditions, road facilities, safety protection, and publicity and education.

[0095] Laws and regulations: including the implementation of national laws on electric bicycle management, local legislation and regulatory documents, national standards, local standards, regulations, notices and guidelines, and other policy and regulatory documents;

[0096] Order management: including a series of measures and methods implemented in accordance with national and local legislation to regulate the riding of electric bicycles;

[0097] Vehicle conditions: including the basic vehicle conditions that the electric bicycle must meet after leaving the factory;

[0098] Road facilities: including road infrastructure conditions required for electric bicycles to pass;

[0099] Safety protection: including safety protection devices that electric bicycle riders must wear;

[0100] Publicity and education: including publicity and education measures on safety, law-abidingness, and civility carried out by the traffic management department for electric bicycle riders.

[0101] Establish the evaluation factor matrix E-Bike = {RegulationsAnd Policies, Traffic Management, E-bike Conditions, Road Facilities, Safety Prevention, Safety Education}, where,

[0102] Regulations and Policies = {national laws, local legislation, regulations, rules and other policy normative documents, notices, standards and guidelines...};

[0103] Traffic Management = {right of way, vehicle attributes, restricted passage, prohibited passage, speed limit reminder, traffic organization, driving against traffic, running a red light, illegal occupation of the road, speeding, carrying passengers, carrying goods, distracted driving, ...};

[0104] Vehicle condition category set E-bike Conditions = {body structure, maximum design speed, vehicle mass, battery, CCC certification, electronic identification, short circuit protection, leakage protection, GPS positioning, ABS, ...};

[0105] Road Facilities = {traffic signs, traffic markings, non-motorized vehicle lanes, traffic lights, isolation barriers, calming facilities, vehicle blocking piles, vibrating markings, reflective road studs, non-motorized vehicle signal lights, ...};

[0106] Safety prevention category set Safety Prevention = {safety helmet, child safety seat, reflective vest, goggles, avoid pedestrians, ...};

[0107] The publicity and education category set Safety Education = {obey traffic rules, cycling safety awareness, civilized cycling, warning education, children's safety, green travel, elderly safety, slow down, ...}.

[0108] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating the effectiveness of electric bicycle management, characterized in that: The following steps are involved: Extracting electric bicycle violation and accident data; Based on the logic analysis method, the electric bicycle violation and accident data are analyzed to obtain various causes, and an evaluation factor matrix is ​​constructed; The rank sum ratio method is used to extract and calculate various causes and obtain the weight of each cause; Based on the regression tree algorithm, the weight of each cause and the evaluation factor matrix are matched to obtain the specific cause effectiveness of electric bicycle management.

2. The method for evaluating the efficiency of electric bicycle management according to claim 1, characterized in that: The evaluation factor matrix includes: a law and policy category set, an order management category set, a vehicle condition category set, a road facility category set, a safety protection category set and a publicity and education category set.

3. The method for evaluating the efficiency of electric bicycle management according to claim 2, characterized in that: The regulatory policy category set includes: national laws, local legislation, regulations and guidelines, rules and regulations, policy normative documents, notices and notifications, and standard guidelines; The order management category set includes: road rights, vehicle attributes, restricted passage, prohibited passage, speed limit reminder, traffic organization, driving against traffic, running a red light, illegal occupation of the road, speeding, carrying people, carrying goods, and distracted driving; The vehicle condition category set includes: body structure, maximum design speed, vehicle mass, battery, CCC certification, electronic identification, short circuit protection, leakage protection, GPS positioning, and ABS; The road facility category set includes: traffic signs, traffic markings, non-motorized vehicle lanes, traffic lights, isolation guardrails, stabilization facilities, vehicle blocking piles, vibrating markings, reflective road studs, and non-motorized vehicle signal lights; The safety protection category set includes: safety helmet, child safety seat, reflective vest, goggles, and pedestrian avoidance; The publicity and education category set includes: compliance with traffic rules, cycling safety awareness, civilized cycling, warning education, children's safety, green travel, elderly safety, and slowing down.

4. The method for evaluating the efficiency of electric bicycle management according to claim 1, characterized in that: Before extracting and calculating the causes using the rank sum ratio method, the method also includes ranking the evaluation object of each evaluation factor; Among them, electric bicycles are ranked in ascending order according to the severity of violations and accidents; electric bicycles are ranked in descending order according to management strategies and means; and when the values ​​of several evaluation objects on specific evaluation factors are exactly the same, an average ranking is performed.

5. The method for evaluating the efficiency of electric bicycle management according to claim 4, characterized in that: The process of extracting and calculating the causes using the rank sum ratio method includes: Calculate the rank sum ratio of the evaluation object based on the ranked evaluation object; The rank sum ratio method was used to calculate the cumulative frequency of each cause; The linear regression equation was calculated based on the cumulative frequency and rank sum ratio of each cause; The weight of each cause is calculated based on the linear regression equation.

6. The method for evaluating the efficiency of electric bicycle management according to claim 5, characterized in that: The method of evaluating the effectiveness of electric bicycle management also includes calculating the overall effectiveness of electric bicycle management based on the weights of various causes and extracting the total amount of electric bicycle violations and accident data; Among them, the formula for calculating the overall effectiveness of electric bicycle management is: In the formula, τ represents the overall effectiveness of electric bicycle management, ε represents the calculated value of the regression equation, and γ represents the total amount of electric bicycle violation and accident data extracted.

7. The method for evaluating the efficiency of electric bicycle management according to claim 6, characterized in that: The process of calculating the specific cause effectiveness of electric bicycle governance includes: Calculate the average cause weight value based on the weight of each cause; Calculate the evaluation factor benchmarks of various causes based on the average cause weight value; Calculate the average violation and accident value based on the total amount of electric bicycle violation and accident data extracted; Calculate the assessment level of each cause based on the average violation and incident values; The evaluation factor benchmarks and evaluation levels of various factors are compared respectively to obtain the specific causal effectiveness of electric bicycle management.

8. A system for evaluating the effectiveness of electric bicycle management, characterized in that: include: Data extraction module, cause analysis module, factor classification module, evaluation factor matrix construction module, ranking module, RSR calculation module and distribution determination module; Wherein, the data extraction module is used to extract electric bicycle violation and accident data from the public security traffic management database; The cause analysis module is used to analyze the causes of electric bicycle violations and accidents using logic analysis; The factor classification module is used to classify the causes into six categories: laws and policies, order management, vehicle conditions, road facilities, safety protection and publicity and education; The evaluation factor matrix construction module is used to construct an evaluation factor matrix; The ranking module is used to rank the evaluation object of each evaluation factor; The RSR calculation module is used to calculate the rank sum ratio and the weighted rank sum ratio; The governance effectiveness determination module determines the weight of each cause based on the rank sum ratio and matches the weight of each cause with the evaluation factor matrix to obtain the specific cause effectiveness of electric bicycle governance.

Images