A multi-dimensional index-based online car-hailing scheduling effect evaluation method

Abstract

The application relates to a multi-dimensional index-based online car-hailing scheduling effect evaluation method, relates to the operation and scheduling management technical field of an online car-hailing platform, and comprises the following steps: acquiring multi-dimensional index data of an online car-hailing scheduling system; based on a preset evaluation index system and a comment set, quantitatively processing the multi-dimensional index data to obtain quantized evaluation information, the quantized evaluation information representing information that each evaluation index belongs to different evaluation grades, and the comment set comprising multiple evaluation grades; based on the quantized evaluation index information, constructing a fuzzy relationship matrix, the fuzzy relationship matrix representing the fuzzy relationship between the evaluation indexes and the evaluation grades; based on a preset weight distribution rule, determining the weight information of each evaluation index in the quantized evaluation index information; and based on the weight information and the fuzzy relationship matrix, performing fuzzy synthesis operation to determine the fuzzy comprehensive evaluation result of the online car-hailing scheduling effect. The application realizes fine and quantitative judgment on the comprehensive performance of a scheduling strategy under multi-dimensional indexes.

Description

Technical Field

[0001] This invention relates to the field of operation and dispatch management technology for ride-hailing platforms, and in particular to a method for evaluating the dispatch effectiveness of ride-hailing services based on multi-dimensional indicators. Background Technology

[0002] With the deep integration of mobile internet and the sharing economy, ride-hailing has become an important mode of urban transportation, and its core operational efficiency highly depends on the platform's intelligent dispatch system. Currently, technological research and development in the industry mainly focuses on improving order response speed and vehicle utilization by optimizing real-time matching algorithms.

[0003] Currently, ride-hailing platforms' dispatch systems have mature order distribution and vehicle matching functions. However, these systems generally focus on the instantaneous efficiency of the dispatch process, and usually only use one or a few indicators such as order completion rate and average response time to measure the dispatch effect afterward.

[0004] However, this evaluation method fails to systematically incorporate multi-dimensional factors such as user satisfaction, driver income balance, and regional supply-demand matching. This makes it difficult to comprehensively and scientifically quantify the overall performance of the dispatch system, and also fails to provide accurate and reliable feedback for the continuous optimization of dispatch algorithms and the platform's refined operational decisions. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for evaluating the scheduling effect of ride-hailing services based on multi-dimensional indicators, in order to solve at least one of the above-mentioned technical problems.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: Firstly, this application provides a multi-dimensional indicator method for evaluating the effectiveness of ride-hailing dispatching, employing the following technical solution: A method for evaluating ride-hailing dispatch effectiveness based on multi-dimensional indicators includes: The system acquires multi-dimensional indicator data from the ride-hailing dispatch system. This multi-dimensional indicator data includes historical order data, real-time vehicle location and status data, traffic information, and user feedback data. The historical order data includes ride-hailing order data and order completion data. Based on a preset evaluation index system and comment set, the multi-dimensional index data is quantified to obtain quantified evaluation information. The quantified evaluation information represents the information that each evaluation index belongs to a different evaluation level. The comment set includes multiple evaluation levels. Based on quantitative evaluation index information, a fuzzy relation matrix is ​​constructed, which represents the fuzzy relationship between the evaluation index and the evaluation level. Based on the preset weight allocation rules, the weight information of each evaluation indicator in the quantitative evaluation indicator information is determined; Based on the weight information and the fuzzy relation matrix, a fuzzy synthesis operation is performed to determine the fuzzy comprehensive evaluation result of the ride-hailing dispatch effect.

[0007] The beneficial effects of this invention are as follows: It adapts to the differentiated evaluation needs of different business scenarios through an evaluation index system; it adopts a comprehensive weighting strategy combining the Analytic Hierarchy Process (AHP) and entropy weighting, which incorporates both expert domain knowledge and strategic intent while fully mining the objective information of the data itself, effectively overcoming evaluation biases caused by subjective assumptions or data bias in traditional methods, ensuring the objectivity and accuracy of the evaluation results; and by establishing a fuzzy relation matrix and performing fuzzy synthesis operations, it organically unifies qualitative evaluation and quantitative analysis, accurately handling the inherent fuzziness and uncertainty in scheduling effect evaluation, and achieving a refined and quantitative evaluation of the comprehensive effectiveness of scheduling strategies under multi-dimensional indicators.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the evaluation index system includes multiple metrics such as dispatch response time, vehicle empty-run rate, service punctuality rate, peak-hour dispatch capability, and customer satisfaction. Based on the preset evaluation index system and comment set, the multi-dimensional index data is quantified to obtain quantified evaluation index information, including: For each evaluation indicator in the evaluation indicator system, based on the multi-dimensional indicator data, calculate the degree of membership of the evaluation indicator to each evaluation level in the comment set; Based on the membership degree of each evaluation level corresponding to each of the aforementioned evaluation indicators, quantitative evaluation indicator information is determined.

[0010] The beneficial effects of adopting the above-mentioned further solution are: by comprehensively considering multiple evaluation indicators such as dispatch response time, vehicle empty-run rate, service punctuality rate, peak-hour dispatch capability, and customer satisfaction, the multi-dimensional indicator data is quantified, and the membership degree of each evaluation indicator to each evaluation level in the comment set is calculated. This determines the quantitative evaluation indicator information, avoids the one-sidedness of a single indicator, achieves a more comprehensive and accurate evaluation of ride-hailing dispatch effectiveness, provides a basis for optimizing dispatch decisions, improves the overall operational efficiency of the platform, and solves the problem of the limitations of a single evaluation indicator in the existing dispatch evaluation system.

[0011] Furthermore, the fuzzy relation matrix is ​​as follows: ; Where n represents the level in the comment set, and m represents the number of specific indicators in the evaluation indicator system. This represents the single-factor evaluation vector for the i-th evaluation indicator. This indicates the degree to which the i-th evaluation indicator belongs to the j-th evaluation level.

[0012] The beneficial effect of adopting the above-mentioned further scheme is to achieve a more accurate and comprehensive fuzzy integrated evaluation of the ride-hailing dispatch effect.

[0013] Furthermore, the determination of the weight information of each evaluation indicator in the quantitative evaluation indicator information based on the preset weight allocation rule includes: Obtain expert decision-making information; Based on expert decision-making information, the first weight vector of each evaluation index is calculated using the analytic hierarchy process. Based on the fuzzy relation matrix, the information entropy and entropy weight of each evaluation index are calculated using the entropy weight method to obtain the second weight vector of each evaluation index. Based on the first weight vector and the second weight vector, the weight information of each evaluation indicator in the quantitative evaluation indicator information is determined.

[0014] The beneficial effects of adopting the above-mentioned further scheme are as follows: by obtaining expert decision-making information and using the analytic hierarchy process (AHP) to calculate the first weight vector, professional experience and strategic intent are reflected; the second weight vector is calculated based on the fuzzy relation matrix using the entropy weight method, and the weights are determined according to the dispersion of the data itself, which is data-driven; by combining the first weight vector and the second weight vector to determine the weight information of each evaluation indicator, subjective intent and objective facts are taken into account, ensuring the scientificity and accuracy of weight allocation when evaluating the effectiveness of ride-hailing dispatch.

[0015] Furthermore, based on the fuzzy relation matrix, the entropy and entropy weight of each evaluation index are calculated using the entropy weight method to obtain the second weight vector of each evaluation index, including: Based on the fuzzy relation matrix and the preset information entropy calculation formula, the information entropy of each evaluation index is determined. Based on the information entropy of each evaluation indicator and the preset entropy weight calculation formula, the entropy weight of each evaluation indicator is calculated. Based on the entropy weight of each evaluation indicator, determine the second weight vector of each evaluation indicator; The preset formula for calculating information entropy is: ; Where n is the number of evaluation levels in the set of comments, and m is the total number of indicators in the evaluation indicator system. This represents the probability distribution of the data for the j-th evaluation indicator at the r-th evaluation level, where k is a constant. ; The preset formula for calculating entropy weight is as follows: ; in, It is the sum of the information entropy of all m evaluation indicators.

[0016] The beneficial effects of adopting the above-mentioned further scheme are as follows: by combining the fuzzy relation matrix and the preset formula to calculate the information entropy and entropy weight of each evaluation index, a second weight vector is obtained. The weight of each evaluation index can be determined according to the dispersion of the data itself, realizing data-driven weight allocation, making the evaluation results more objective, providing an accurate weight basis for subsequent fuzzy synthesis operations to determine the fuzzy comprehensive evaluation results of the ride-hailing dispatch effect, accurately evaluating the ride-hailing dispatch effect, optimizing dispatch decisions, improving vehicle resource utilization, and reducing operating costs.

[0017] Furthermore, the step of calculating the first weight vector of each evaluation index based on expert decision-making information using the analytic hierarchy process includes: Based on expert decision-making information, a judgment matrix is ​​constructed. The judgment matrix includes multiple rows of elements, and each element represents the importance scale value of the expert for the i-th evaluation indicator relative to the j-th evaluation indicator. Calculate the geometric mean of all elements in each row of the judgment matrix; The geometric mean of each evaluation index is normalized to obtain the subjective weight of each evaluation index. Consistency checks are performed on the subjective weights of each evaluation indicator, the consistency ratio is calculated, and when the consistency ratio is less than the preset threshold, the vector composed of the subjective weights of each evaluation indicator is used as the first weight vector.

[0018] The beneficial effects of adopting the above-mentioned further scheme are as follows: By constructing a judgment matrix based on expert decision-making information, professional experience and strategic intent are reflected, and the experts' judgments on the importance of different evaluation indicators are reflected; by calculating the geometric mean of each row of the judgment matrix and performing normalization, the subjective weights of each evaluation indicator can be obtained; by performing consistency checks on the subjective weights, the rationality and scientific nature of the weights can be ensured; when the consistency ratio is less than the preset ratio threshold, the vector composed of subjective weights is used as the first weight vector, which can ensure that the subsequent scheduling evaluation based on this weight vector is more accurate and reliable, taking into account subjective intent, and providing a scientific basis for weight allocation for the comprehensive evaluation of the ride-hailing scheduling effect.

[0019] Furthermore, the formula for fuzzy synthesis is as follows: ; in, This represents the fuzzy comprehensive evaluation result vector, which characterizes the overall degree of membership of the scheduling effect at different evaluation levels.

[0020] The beneficial effects of adopting the above-mentioned further scheme are: based on the weight information of the evaluation indicators and the fuzzy relation matrix, fuzzy synthesis operation is performed to determine the fuzzy comprehensive evaluation result that represents the overall membership degree of the entire scheduling effect at different evaluation levels, providing a quantitative comprehensive evaluation of the ride-hailing scheduling effect, which helps ride-hailing platforms to fully understand the scheduling effect and thus optimize scheduling decisions.

[0021] Secondly, this application provides a ride-hailing dispatch effectiveness evaluation device based on multi-dimensional indicators, employing the following technical solution: A ride-hailing dispatch performance evaluation device based on multi-dimensional indicators includes: The data acquisition module is used to acquire multi-dimensional indicator data from the ride-hailing dispatch system. The multi-dimensional indicator data includes historical order data, real-time vehicle location and status data, traffic information and user feedback data. The historical order data includes ride-hailing order data and order completion data. The evaluation feedback module is used to quantify the multi-dimensional indicator data based on a preset evaluation indicator system and a set of comments to obtain quantified evaluation indicator information. The evaluation indicator reminders include multiple indicators such as dispatch response time, vehicle empty running rate, service on-time rate, peak dispatch capability, and customer satisfaction. The set of comments includes multiple evaluation levels. A construction module is used to construct a fuzzy relation matrix based on quantitative evaluation index information. The fuzzy relation matrix represents the fuzzy relationship between the index and the evaluation level. The weight allocation module is used to determine the weight information of each evaluation indicator in the quantitative evaluation indicator information based on the preset weight allocation rules. The generation module is used to perform fuzzy synthesis operations based on the weight information and the fuzzy relation matrix to determine the fuzzy comprehensive evaluation result of the ride-hailing dispatch effect.

[0022] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the first aspects, a method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators.

[0023] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in any one of the first aspects, a method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a method for evaluating the dispatching effectiveness of ride-hailing services based on multi-dimensional indicators, as provided in one embodiment of the present invention. Figure 2 A schematic diagram of a ride-hailing dispatch effect evaluation device based on multi-dimensional indicators provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] This application provides a method for evaluating the dispatching effect of ride-hailing services based on multi-dimensional indicators. This method can be executed by an electronic device, which can be a server or a mobile terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The mobile terminal device can be a laptop computer, a desktop computer, etc., but is not limited to these.

[0029] like Figure 1 As shown, a method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators includes: S1, obtain multi-dimensional indicator data from the ride-hailing dispatch system. The multi-dimensional indicator data includes historical order data, real-time vehicle location and status data, traffic information and user feedback data. The historical order data includes ride-hailing order data and order completion data. In this embodiment of the application, historical order data includes ride-hailing order data and order completion data. Order data can reflect user demand in different time periods and regions, while order completion data can reflect the completion status of orders, such as completion time and completion rate.

[0030] Real-time vehicle location and status data can be obtained through the GPS positioning system and related sensors installed in the vehicle, enabling the dispatch system to understand the vehicle's location, availability, and direction of travel in real time.

[0031] Traffic information can be obtained from traffic management department databases, map software, and other channels to understand road congestion, traffic speed, and other information.

[0032] User feedback data can be collected through ride-hailing platforms' rating and complaint systems, directly reflecting users' opinions on dispatch effectiveness and service quality.

[0033] The historical order data here can also be replaced with other data sources that reflect user needs and order status, such as relevant data provided by third-party data statistics agencies; real-time vehicle location and status data can also be obtained through the vehicle's Beidou positioning system.

[0034] S2, based on the preset evaluation index system and comment set, the multi-dimensional index data is quantified to obtain quantified evaluation information. The quantified evaluation information represents the information that each evaluation index belongs to different evaluation levels. The comment set includes multiple evaluation levels. In this embodiment, the evaluation index system includes multiple metrics such as dispatch response time, vehicle empty-run rate, service punctuality rate, peak-hour dispatch capability, and customer satisfaction. Dispatch response time refers to the time interval from when a user places an order to when a driver accepts it, reflecting the dispatch system's responsiveness. Vehicle empty-run rate is the proportion of vehicles that travel without passengers, reflecting the efficiency of vehicle resource utilization. Service punctuality rate is the proportion of drivers arriving at the agreed location on time to pick up and drop off passengers, directly impacting user satisfaction. Peak-hour dispatch capability reflects the system's ability to handle surges in orders during peak hours. Customer satisfaction is reflected through user evaluations and feedback.

[0035] The evaluation set includes multiple evaluation levels, such as five levels: very satisfied, fairly satisfied, average, somewhat dissatisfied, and very dissatisfied.

[0036] In this embodiment of the application, the step of quantifying the multi-dimensional indicator data based on a preset evaluation indicator system and comment set to obtain quantified evaluation indicator information includes: For each evaluation indicator in the evaluation indicator system, based on the multi-dimensional indicator data, calculate the degree of membership of the evaluation indicator to each evaluation level in the comment set; Based on the membership degree of each evaluation level corresponding to each of the aforementioned evaluation indicators, quantitative evaluation indicator information is determined.

[0037] For example, regarding dispatch response time, if less than 5 minutes is considered "very satisfactory," and 5-10 minutes is "relatively satisfactory," then based on actual dispatch response time data, the proportion of each evaluation level (i.e., membership degree) is calculated. Then, based on the membership degree of each evaluation indicator for each corresponding evaluation level, quantitative evaluation indicator information is determined. This evaluation indicator system can also be adjusted according to actual needs, adding or removing some indicators; the evaluation level division of the comment set can also be changed according to specific circumstances.

[0038] S3. Based on the quantitative evaluation index information, a fuzzy relation matrix is ​​constructed, which represents the fuzzy relationship between the evaluation index and the evaluation level. In this embodiment of the application, the fuzzy relation matrix is: ; Where n represents the level in the comment set, and m represents the number of specific indicators in the evaluation indicator system. This represents the single-factor evaluation vector for the i-th evaluation indicator. This indicates the degree to which the i-th evaluation indicator belongs to the j-th evaluation level.

[0039] The fuzzy relation matrix clearly shows the membership of each evaluation indicator at different evaluation levels. Other mathematical models or algorithms can also be used to construct this fuzzy relation matrix, as long as they accurately represent the relationship between the evaluation indicators and the evaluation levels.

[0040] S4, based on the preset weight allocation rules, determine the weight information of each evaluation indicator in the quantitative evaluation indicator information; In this embodiment of the application, determining the weight information of each evaluation indicator in the quantitative evaluation indicator information based on a preset weight allocation rule includes: Obtain expert decision-making information; Based on expert decision-making information, the first weight vector of each evaluation index is calculated using the analytic hierarchy process. Based on the fuzzy relation matrix, the information entropy and entropy weight of each evaluation index are calculated using the entropy weight method to obtain the second weight vector of each evaluation index. Based on the first weight vector and the second weight vector, the weight information of each evaluation indicator in the quantitative evaluation indicator information is determined.

[0041] In the above embodiments, the step of calculating the first weight vector of each evaluation index based on expert decision information using the analytic hierarchy process includes: Based on expert decision-making information, a judgment matrix is ​​constructed. The judgment matrix includes multiple rows of elements, and each element represents the importance scale value of the expert for the i-th evaluation indicator relative to the j-th evaluation indicator. Calculate the geometric mean of all elements in each row of the judgment matrix; The geometric mean of each evaluation index is normalized to obtain the subjective weight of each evaluation index. Consistency checks are performed on the subjective weights of each evaluation indicator, the consistency ratio is calculated, and when the consistency ratio is less than the preset threshold, the vector composed of the subjective weights of each evaluation indicator is used as the first weight vector.

[0042] Experts can be seasoned professionals in the ride-hailing industry, scholars in the transportation field, etc. Their experience and expertise can provide important references for weight allocation. Based on expert decision-making information, the first weight vector of each evaluation indicator is calculated using the analytic hierarchy process (AHP). The AHP constructs a judgment matrix through expert scoring. The judgment matrix includes multiple rows of elements, each representing the importance scale value of the expert for the i-th evaluation indicator relative to the j-th evaluation indicator.

[0043] The geometric mean of all elements in each row of the judgment matrix is ​​calculated using the following formula: ; in, Let be the geometric mean of all elements in the i-th row of the judgment matrix corresponding to the i-th indicator, where n is the total number of evaluation indicators. To determine the element in the i-th row and j-th column of a matrix.

[0044] Next, a consistency check is performed on the obtained matrix using the following formula, resulting in the final value. The value is less than 0.001; ; in, Let be the normalized weight of the i-th indicator; ; ; in, To determine the largest eigenvalue of a matrix, Let be the i-th component of the vector AW. AW is the product of the judgment matrix and the calculated weight vector W, CI is the consistency index, RI is the average random consistency index, and CR is the consistency ratio.

[0045] In the above implementation methods, the Delphi method can also be used to obtain expert decision-making information. The Delphi method is a method that solicits expert opinions and gradually reaches a consensus through multiple rounds of anonymous questionnaires. First, a detailed questionnaire is designed, including an evaluation index system and related questions, and distributed to selected experts. Experts answer the questionnaire based on their experience and knowledge. Then, the experts' opinions are summarized and organized, removing duplicate and invalid information. The questionnaire is then distributed to the experts again, allowing them to refer to the summary results from the previous round and adjust and supplement their opinions. This process is repeated multiple times until the experts' opinions are basically consistent. Based on the expert decision-making information obtained in this way, subsequent steps such as calculating the first weight vector using the analytic hierarchy process (AHP) are then performed.

[0046] In the above embodiments, the step of calculating the information entropy and entropy weight of each evaluation index based on the fuzzy relation matrix and using the entropy weight method to obtain the second weight vector of each evaluation index includes: Based on the fuzzy relation matrix and the preset information entropy calculation formula, the information entropy of each evaluation index is determined. Based on the information entropy of each evaluation indicator and the preset entropy weight calculation formula, the entropy weight of each evaluation indicator is calculated. Based on the entropy weight of each evaluation indicator, determine the second weight vector of each evaluation indicator; The preset formula for calculating information entropy is: ; Where n is the number of evaluation levels in the set of comments, and m is the total number of indicators in the evaluation indicator system. This represents the probability distribution of the data for the j-th evaluation indicator at the r-th evaluation level, where k is a constant. ; The preset formula for calculating entropy weight is as follows: ; in, It is the sum of the information entropy of all m evaluation indicators.

[0047] In the above embodiments, the weight information of each evaluation indicator in the quantitative evaluation indicator information obtained by the analytic hierarchy process and the entropy weight method is... The specific formula is as follows: S5. Based on the weight information and the fuzzy relation matrix, perform fuzzy synthesis operation to determine the fuzzy comprehensive evaluation result of the ride-hailing dispatch effect. Based on the fuzzy comprehensive evaluation result, determine the level of dispatch effect and generate corresponding dispatch strategy optimization suggestions.

[0048] In this embodiment of the application, the fuzzy synthesis operation formula is: ; in, This represents the fuzzy comprehensive evaluation result vector, which characterizes the overall degree of membership of the scheduling effect at different evaluation levels.

[0049] This method, through its evaluation index system, adapts to the differentiated assessment needs of various business scenarios. It employs a comprehensive weighting strategy combining the Analytic Hierarchy Process (AHP) and entropy weighting, incorporating both expert domain knowledge and strategic intent while fully leveraging the objective information inherent in the data. This effectively overcomes assessment biases caused by subjective assumptions or data bias in traditional methods, ensuring the objectivity and accuracy of the assessment results. By establishing a fuzzy relation matrix and performing fuzzy synthesis operations, it organically unifies qualitative evaluation and quantitative analysis, accurately addressing the inherent fuzziness and uncertainty in scheduling effectiveness assessment. This enables a refined and quantitative evaluation of the comprehensive effectiveness of scheduling strategies under multi-dimensional indicators.

[0050] Figure 2 This is a schematic diagram of a ride-hailing dispatch effect evaluation device 200 based on multi-dimensional indicators.

[0051] like Figure 2 As shown, a ride-hailing dispatch performance evaluation device 200 based on multi-dimensional indicators mainly includes: The data acquisition module 201 acquires multi-dimensional indicator data from the ride-hailing dispatch system. The multi-dimensional indicator data includes historical order data, real-time vehicle location and status data, traffic information and user feedback data. The historical order data includes ride-hailing order data and order completion data. The evaluation feedback module 202 is used to quantify the multi-dimensional indicator data based on a preset evaluation indicator system and a set of comments to obtain quantified evaluation indicator information. The evaluation indicator reminders include multiple indicators such as dispatch response time, vehicle empty running rate, service punctuality rate, peak dispatch capability, and customer satisfaction. The set of comments includes multiple evaluation levels. The construction module 203 is used to construct a fuzzy relation matrix based on quantitative evaluation index information, wherein the fuzzy relation matrix represents the fuzzy relationship between the index and the evaluation level; The weight allocation module 204 is used to determine the weight information of each evaluation indicator in the quantitative evaluation indicator information based on the preset weight allocation rules. The generation module 205 is used to perform fuzzy synthesis operation based on the weight information and the fuzzy relation matrix to determine the fuzzy comprehensive evaluation result of the ride-hailing dispatch effect.

[0052] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0053] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0054] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0055] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0056] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0057] Figure 3 This is a structural block diagram of an electronic device 300 according to an embodiment of this application.

[0058] like Figure 3As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0059] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps in the aforementioned method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0060] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used to test wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0061] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0062] The electronic device 300 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the ride-hailing dispatch effect evaluation method based on multi-dimensional indicators given in the above embodiments.

[0063] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium described below can be referred to in correspondence with the ride-hailing dispatch effect evaluation method based on multi-dimensional indicators described above.

[0064] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators.

[0065] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0067] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators, characterized in that, include: The system acquires multi-dimensional indicator data from the ride-hailing dispatch system. This multi-dimensional indicator data includes historical order data, real-time vehicle location and status data, traffic information, and user feedback data. The historical order data includes ride-hailing order data and order completion data. Based on a preset evaluation index system and comment set, the multi-dimensional index data is quantified to obtain quantified evaluation information. The quantified evaluation information represents the information that each evaluation index belongs to a different evaluation level. The comment set includes multiple evaluation levels. Based on quantitative evaluation index information, a fuzzy relation matrix is ​​constructed, which represents the fuzzy relationship between the evaluation index and the evaluation level. Based on the preset weight allocation rules, the weight information of each evaluation indicator in the quantitative evaluation indicator information is determined; Based on the weight information and the fuzzy relation matrix, a fuzzy synthesis operation is performed to determine the fuzzy comprehensive evaluation result of the ride-hailing dispatch effect.

2. The method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators as described in claim 1, characterized in that, The evaluation index system includes multiple metrics such as dispatch response time, vehicle empty-run rate, service punctuality rate, peak-hour dispatch capability, and customer satisfaction. Based on the preset evaluation index system and comment set, the multi-dimensional index data is quantified to obtain quantified evaluation index information, including: For each evaluation indicator in the evaluation indicator system, based on the multi-dimensional indicator data, calculate the degree of membership of the evaluation indicator to each evaluation level in the comment set; Based on the membership degree of each evaluation level corresponding to each of the aforementioned evaluation indicators, quantitative evaluation indicator information is determined.

3. The method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators as described in claim 1, characterized in that, The fuzzy relation matrix is: ； Where n represents the level in the comment set, and m represents the number of specific indicators in the evaluation indicator system. This represents the single-factor evaluation vector for the i-th evaluation indicator. This indicates the degree to which the i-th evaluation indicator belongs to the j-th evaluation level.

4. The method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators as described in claim 1, characterized in that, The determination of the weight information of each evaluation indicator in the quantitative evaluation indicator information based on the preset weight allocation rule includes: Obtain expert decision-making information; Based on expert decision-making information, the first weight vector of each evaluation index is calculated using the analytic hierarchy process. Based on the fuzzy relation matrix, the information entropy and entropy weight of each evaluation index are calculated using the entropy weight method to obtain the second weight vector of each evaluation index. Based on the first weight vector and the second weight vector, the weight information of each evaluation indicator in the quantitative evaluation indicator information is determined.

5. The method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators as described in claim 4, characterized in that, The method based on the fuzzy relation matrix, using the entropy weight method to calculate the information entropy and entropy weight of each evaluation index, yields the second weight vector for each evaluation index, including: Based on the fuzzy relation matrix and the preset information entropy calculation formula, the information entropy of each evaluation index is determined. Based on the information entropy of each evaluation indicator and the preset entropy weight calculation formula, the entropy weight of each evaluation indicator is calculated. Based on the entropy weight of each evaluation indicator, determine the second weight vector of each evaluation indicator; The preset formula for calculating information entropy is: ； Where n is the number of evaluation levels in the set of comments, and m is the total number of indicators in the evaluation indicator system. This represents the probability distribution of the data for the j-th evaluation indicator at the r-th evaluation level, where k is a constant. ; The preset formula for calculating entropy weight is as follows: ； in, It is the sum of the information entropy of all m evaluation indicators.

6. The method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators as described in claim 4, characterized in that, The first weight vector of each evaluation index, calculated using the analytic hierarchy process (AHP) based on expert decision-making information, includes: Based on expert decision-making information, a judgment matrix is ​​constructed. The judgment matrix includes multiple rows of elements, and each element represents the importance scale value of the expert for the i-th evaluation indicator relative to the j-th evaluation indicator. Calculate the geometric mean of all elements in each row of the judgment matrix; The geometric mean of each evaluation index is normalized to obtain the subjective weight of each evaluation index. Consistency checks are performed on the subjective weights of each evaluation indicator, the consistency ratio is calculated, and when the consistency ratio is less than the preset threshold, the vector composed of the subjective weights of each evaluation indicator is used as the first weight vector.

7. The method for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators as described in claim 1, characterized in that, The formula for fuzzy synthesis is: ； in, This represents the fuzzy comprehensive evaluation result vector, which characterizes the overall degree of membership of the scheduling effect at different evaluation levels.

8. A device for evaluating the effectiveness of ride-hailing dispatch based on multi-dimensional indicators, characterized in that, include: The data acquisition module is used to acquire multi-dimensional indicator data from the ride-hailing dispatch system. The multi-dimensional indicator data includes historical order data, real-time vehicle location and status data, traffic information and user feedback data. The historical order data includes ride-hailing order data and order completion data. The evaluation feedback module is used to quantify the multi-dimensional indicator data based on a preset evaluation indicator system and a set of comments to obtain quantified evaluation indicator information. The evaluation indicator reminders include multiple indicators such as dispatch response time, vehicle empty running rate, service on-time rate, peak dispatch capability, and customer satisfaction. The set of comments includes multiple evaluation levels. A construction module is used to construct a fuzzy relation matrix based on quantitative evaluation index information. The fuzzy relation matrix represents the fuzzy relationship between the index and the evaluation level. The weight allocation module is used to determine the weight information of each evaluation indicator in the quantitative evaluation indicator information based on the preset weight allocation rules. The generation module is used to perform fuzzy synthesis operations based on the weight information and the fuzzy relation matrix to determine the fuzzy comprehensive evaluation result of the ride-hailing dispatch effect.

9. An electronic device, characterized in that, Includes a processor, which is coupled to a memory; The processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1-7.

Images