Smart city service development index compilation method

Abstract

According to the smart city service development index compilation method, a comprehensive, systematic and scientific smart city service development evaluation framework is designed, the overall condition of smart city service development is reflected more comprehensively by constructing index systems in multiple aspects, and one-sided emphasis or neglect on a certain aspect is avoided. The method has measurability and comparability, objective data support can be provided by selecting measurable, statistical and computable indexes, and evaluation of smart city service development is more persuasive. Meanwhile, through comparison with index data of other cities, advantages and disadvantages of the city in the aspect of smart city service development can be clearly known, and a basis is provided for formulating targeted improvement measures. In addition, the method is dynamic and practical, can adjust and perfect an index system in time to adapt to new demands of city development, can provide reference and support for government decision making, and promotes sustainable development of smart city construction.

Description

Technical Field

[0001] The present invention relates to the technical field of service development index compilation, and in particular to a method for compiling a smart city service development index. Background Art

[0002] Smart city is a new type of infrastructure built on digital technologies such as cloud computing, mobile Internet, Internet of Things, big data, and artificial intelligence. It is an urban digital space. One of its main purposes is to provide citizens (natural persons), enterprises and institutions (legal persons) and urban management departments with efficient, convenient, and rich public services, and to achieve interaction and linkage with the real world. The health status and development trend of smart city services need to be evaluated by scientific means in order to fully and accurately grasp the current situation, identify problems in a timely and clear manner, and make rational and accurate decisions. The Smart-city Service Development Index (SSDI) is a quantitative evaluation method for digital services that can comprehensively and intuitively reflect the capabilities, quality, and activity of smart city public services.

[0003] Existing quantitative evaluation methods for digital services have the following disadvantages:

[0004] 1. The efficiency of data collection and integration is low, resulting in a delay in the update of the service development index, which cannot timely reflect the real-time changes in the level of urban services. In addition, the existing data processing methods often ignore the complex associations and interactions between data, resulting in deviations in the analysis results of the service development index, which cannot accurately reflect the true status of urban services.

[0005] 2. In the process of constructing the Smart City Service Development Index, there is a lack of a unified and standardized evaluation framework. Different cities or regions often use different evaluation indicators and calculation methods according to their own conditions and needs, resulting in poor comparability and replicability of the service development index. This not only increases the complexity of evaluation and comparison, but also limits the application value of the service development index in urban planning and decision-making.

[0006] 3. With the continuous expansion and upgrading of urban service content, the existing smart city service development index system is often difficult to adapt to new service needs and changes. This requires us to have flexibility and scalability so that we can timely adjust and optimize the construction method and evaluation indicators of the service development index to better reflect the actual situation and development trend of urban services. Summary of the invention

[0007] The purpose of the technical solution of the present invention is to design a method for compiling a smart city service development index to solve the problems of low efficiency in data collection and integration, the existing data processing methods ignoring the complex correlation and interaction between data, the lack of a unified and standardized evaluation framework, and poor flexibility and scalability.

[0008] The technical solution of the present invention provides a method for compiling a smart city service development index, comprising the following steps:

[0009] Select sample indicators, activity indicators, growth indicators, satisfaction indicators and safety indicators for grading incident severity to develop a classification and grading indicator system and determine a quantifiable evaluation indicator set;

[0010] According to the historical data of sample indicators, activity indicators, growth indicators, satisfaction indicators and safety indicators, the corresponding normalized calculation function is selected to determine the corresponding normal change range, mutation range, minimum value and peak value, and the discrete table lookup scoring function is used to score the indicators to obtain the scores of each indicator;

[0011] Assign weights to each indicator score, integrate each indicator to perform regression calculation of the smart city service development index, use historical data to test the output of the regression calculation and perform single and / or multi-indicator optimization and adjustment, and finally obtain a solidified indicator set and parameter set.

[0012] Preferably, the normalized calculation function includes a linear variation function and an exponential variation function.

[0013] Preferably, the sample indicators are used to describe the visits to typical services in the industry and the usage of city codes.

[0014] Preferably, the activity index is used to describe the total daily visits of users and the daily visits of users.

[0015] Preferably, when the overlap between the total daily visits and the typical service visits reaches a preset threshold, the total daily visits are replaced by the service search volume.

[0016] Preferably, the growth index is used to describe the growth of refresh and expansion, including the number of new user registrations and the number of new service releases.

[0017] Preferably, the satisfaction index includes the amount of user complaints and the user satisfaction rate.

[0018] Preferably, the user satisfaction rate is obtained through one or more methods including questionnaire survey, open and secret investigation, and user evaluation.

[0019] Preferably, the security indicators include safety assurance and system stability.

[0020] The technical solution of the present invention provides a method for compiling a smart city service development index, designs a comprehensive, systematic and scientific smart city service development evaluation framework, and can more comprehensively reflect the overall situation of smart city service development by constructing an indicator system in multiple aspects, avoiding one-sided emphasis or neglect of a certain aspect. The technical solution of the present invention is measurable and comparable. By selecting measurable, statistical and calculable indicators, it can provide objective data support, making the evaluation of smart city service development more convincing. At the same time, by comparing with the indicator data of other cities, the advantages and disadvantages of the city in the development of smart city services can be clearly understood, providing a basis for formulating targeted improvement measures. The technical solution of the present invention is also dynamic and practical, and can timely adjust and improve the indicator system to adapt to the new needs of urban development, and can provide reference and support for government decision-making, and promote the sustainable development of smart city construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the SSDI indicator system provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of security indicators and event classification provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of a normalized calculation model for the SSDI index provided in an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of an indicator change curve provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0026] The compilation of the Smart City Service Development Index SSDI consists of three steps: formulating a classification and grading indicator system to determine a set of quantifiable evaluation indicators; determining the normalized calculation function of each indicator separately to suit the data change characteristics of the indicator; setting parameters such as the weight of each indicator, integrating the indicators for regression calculation, using historical data to test the SSDI output, adjusting relevant parameters as needed, repeating each step, and finally solidifying the indicator set and parameter set.

[0027] Assume that: SSDI is calculated daily and its value range is [0,1000]; the normalized value range of each indicator is [0,1000]; taking 10 key indicators as an example, the indicator weights are ωi (i=1...10).

[0028] The SSDI is compiled as follows:

[0029] 1. SSDI Classification and Grading Index System

[0030] Smart city services must focus on both internal conditions such as capabilities and quality, as well as external conditions such as usage and safety. Only by combining these conditions can the different levels of smart city services, such as availability, ease of use, convenience, common use, and love of use, be fully reflected. Therefore, the selected indicators must first be comprehensive and cover all key evaluation factors; second, they must be quantifiable and able to automatically (for example, through a program interface) count and collect indicator data (while being more objective and excluding human intervention); and third, the indicators must be weakly correlated to maintain the independence of changes in each indicator. The embodiment of the present invention adopts an SSDI indicator system divided into five categories and three levels, which is composed as follows (such as Figure 1 shown):

[0031] (1) Sample indicators - composed of the number of visits to the top ten typical services and the number of city code usage. This indicator uses the usage of the most important and most commonly used sample services to reflect the overall situation of the effectiveness of the services. The selection of the top ten typical services covers as many industries as possible (such as social security, medical care, travel, etc.) and is used more frequently. After the typical services are determined, they should be kept as stable as possible over a long period of time to make the data comparable, but individual samples can be adjusted as needed on a regular basis (for example, once a year).

[0032] (2) Activity index - composed of daily total visits (PV) and daily user visits (UV). This index reflects the overall activity of the system, especially the daily user visits, which directly reflects the user participation. The comparison of the previous and next data can reflect the system's stickiness to users, that is, the attractiveness to users and the satisfaction of their needs. The daily total visits overlap with the typical service visits to a certain extent and can be replaced by the service search volume.

[0033] (3) Growth indicators - composed of the number of new user registrations and the number of new service releases. This indicator reflects the growth of system refresh and expansion.

[0034] (4) Satisfaction index - composed of user complaints and user satisfaction rate. User complaints are negative indicators and are used to deduct the total score; user satisfaction rate can be obtained through questionnaires, open and secret visits, user evaluations, etc. Both reflect direct feedback from users on service quality.

[0035] (5) Safety indicators - composed of safety assurance and system stability. Figure 2As shown in the figure, both are negative indicators, and the events are graded into three levels according to the severity. Taking the total score of 100 as an example, the number of events of different levels affects the score to different degrees, and presents a nonlinear change (reflecting the punitiveness from nothing to something and the sensitivity to change).

[0036] 2. SSDI index normalization calculation model

[0037] Different SSDI indicators have different meanings and changing rules. It is necessary to determine the normal change range, mutation range, minimum value and peak value of the data based on the recent historical data, so that the calculation results can reflect the changes with observation significance. For example, the normal range reflects the increase and decrease more linearly, and is more sensitive to sudden growth (often affected by sudden events). It can also be used to meet the needs of encouraging investment to promote growth (such as Figure 3 shown).

[0038] Data normalization processing can be based on different evaluation requirements, and the following corresponding calculation functions can be selected (where f is the output score, x is the input independent variable data, a and b are parameters, and d is the score):

[0039] (1) Linear change function:

[0040] f=ax+b

[0041] (2) Exponential change function:

[0042] f=a+e bx

[0043] (3) Discrete lookup table scoring function:

[0044]

[0045] III. SSDI Index Regression Calculation and Tuning

[0046] The SSDI regression calculation adopts the weighted summation method. First, the 10 index scores fi (i = 1...10) are assigned weights ωi (i = 1...10) respectively, so that ∑ωi = 100%, and the calculation is performed using the following formula:

[0047]

[0048] Use recent historical data to calculate daily SSDI and examine whether the index changes truly reflect the development status and changes of smart city services. If there are deficiencies, recalculate and evaluate by adjusting the calculation parameters and weight values. After being put into operation, the daily SSDI can form an evolution curve, and the change curves of various indicators can be drawn synchronously (such as Figure 4 As shown in the figure, the development trend of SSDI and the impact of various indicators are presented.

[0049] The key points and points to be protected of the specific embodiments of the present invention are as follows:

[0050] 1. A calculation model suitable for normalization of smart city service evaluation indicators, using different mathematical functions to present the changing characteristics of multiple categories of indicators;

[0051] 2. The regression analysis model of multi-indicator integration of smart city services can be adjusted vertically (single indicator) and horizontally (multiple indicators) through parameter correction, so that the output SSDI index can achieve the unity of stability and sensitivity.

Claims

1. A method for compiling a smart city service development index, characterized in that: The following steps are involved: Select sample indicators, activity indicators, growth indicators, satisfaction indicators, and safety indicators for grading incident severity to develop a classification and grading indicator system and determine a quantifiable evaluation indicator set; Based on the historical data of sample indicators, activity indicators, growth indicators, satisfaction indicators, and safety indicators, the corresponding normalization calculation function is selected to determine the corresponding normal variation range, mutation range, minimum value, and peak value. The discrete table lookup scoring function is used to assign points to the indicators to obtain the scores of each indicator. Assign weights to each indicator score, integrate each indicator to perform regression calculation of the smart city service development index, use historical data to test the output of the regression calculation, and perform single and / or multiple indicator optimization adjustments to ultimately obtain a solidified indicator set and parameter set.

2. A method for compiling a smart city service development index according to claim 1, characterized in that: The normalization calculation function includes a linear change function and an exponential change function.

3. The method for compiling a smart city service development index according to claim 1, wherein: The sample indicators are used to describe the visits to typical services in the industry and the usage of city codes.

4. A method for compiling a smart city service development index according to claim 3, characterized in that: The activity index is used to describe the total daily visits and daily visits of users.

5. A method for compiling a smart city service development index according to claim 4, characterized in that: When the degree of overlap between the total daily visits and the typical service visits reaches a preset threshold, the total daily visits are replaced by the service search volume.

6. The method for compiling a smart city service development index according to claim 1, wherein: The growth indicators are used to describe the growth of refresh and expansion, including the number of new user registrations and the number of new service releases.

7. The method for compiling a smart city service development index according to claim 1, wherein: The satisfaction index includes the number of user complaints and the user satisfaction rate.

8. A method for compiling a smart city service development index according to claim 7, characterized in that: The user satisfaction rate is obtained through one or more methods including questionnaires, open and secret inspections, and user evaluations.

9. The method for compiling a smart city service development index according to claim 7, wherein: The safety indicators include safety assurance and system stability.

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