CN113657709A - Site selection method and site selection device - Google Patents

Site selection method and site selection device Download PDF

Info

Publication number
CN113657709A
CN113657709A CN202110764906.0A CN202110764906A CN113657709A CN 113657709 A CN113657709 A CN 113657709A CN 202110764906 A CN202110764906 A CN 202110764906A CN 113657709 A CN113657709 A CN 113657709A
Authority
CN
China
Prior art keywords
candidate
factors
sub
influence
factor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110764906.0A
Other languages
Chinese (zh)
Inventor
秦卫忠
杨晨
韩保华
王善忠
胥晓冬
江张杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Jiahuan Technology Co Ltd
Original Assignee
Nanjing Jiahuan Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Jiahuan Technology Co Ltd filed Critical Nanjing Jiahuan Technology Co Ltd
Priority to CN202110764906.0A priority Critical patent/CN113657709A/en
Publication of CN113657709A publication Critical patent/CN113657709A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Educational Administration (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Primary Health Care (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The embodiment of the application provides an addressing method, which comprises the following steps: determining the numerical values of a plurality of influence factors of each candidate address; calculating the weight of a plurality of influence factors corresponding to the plurality of influence factors of each candidate address; calculating scores of the plurality of influence factors of each candidate address according to the numerical values of the plurality of influence factors of each candidate address and the weights of the plurality of influence factors; calculating a composite score of each candidate address according to the scores of the plurality of influence factors of each candidate address; and determining a final address from the plurality of candidate addresses according to the comprehensive scores of the plurality of candidate addresses. The address selection process has the support of a large amount of data such as influencing factors, weights and the like, and the data such as the influencing factors, the weights and the like are subjected to quantitative processing such as calculation and the like, so that the rationality of the final address selection is improved.

Description

Site selection method and site selection device
Technical Field
The present application relates to the field of address selection technologies, and in particular, to an address selection method and an address selection apparatus, as well as an electronic device and a computer-readable storage medium.
Background
With the increase of shared cars or shared bicycles, in order to better perform unified management on the shared cars or shared bicycles, a plurality of parking stations for the shared cars or shared bicycles need to be built. Currently, the selection of stations such as shared cars or shared bicycles and the like basically depends on subjective selection of people on understanding of regional conditions, or station selection is performed according to previous station selection experience, so that the station selection is unreasonable.
Disclosure of Invention
In view of this, embodiments of the present application provide an addressing method and an addressing device, an electronic device, and a computer-readable storage medium, which solve the problem of unreasonable site selection.
According to an aspect of the present application, an addressing method provided in an embodiment of the present application includes: determining the numerical values of a plurality of influence factors of each candidate address; calculating the weight of a plurality of influence factors corresponding to the plurality of influence factors of each candidate address; calculating scores of the plurality of influence factors of each candidate address according to the numerical values of the plurality of influence factors of each candidate address and the weights of the plurality of influence factors; calculating a composite score of each candidate address according to the scores of the plurality of influence factors of each candidate address; and determining a final address from the plurality of candidate addresses according to the comprehensive scores of the plurality of candidate addresses.
In an embodiment of the application, the determining the values of the plurality of influencing factors of each candidate address includes: determining a number of each of a plurality of sub-factors of influence; and determining the values of the plurality of influencing factors of each candidate address according to the values of the plurality of sub-influencing factors of each influencing factor.
In an embodiment of the application, the determining the values of the plurality of influencing factors for each of the candidate addresses according to the values of the plurality of sub-influencing factors for each of the influencing factors includes: calculating the weight of a plurality of sub-influence factors corresponding to a plurality of sub-influence factors of each influence factor; and calculating the numerical values of the plurality of influence factors of each candidate address according to the numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
In an embodiment of the application, the calculating, according to the values of the multiple sub-influence factors of each of the influence factors and the weights of the corresponding multiple sub-influence factors, the values of the multiple influence factors of each of the candidate addresses includes: normalizing the numerical values of the multiple sub-influence factors of each influence factor to obtain normalized numerical values of the multiple sub-influence factors of each influence factor; and calculating the numerical values of the plurality of influence factors of each candidate address according to the standardized numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
In an embodiment of the present application, the method further includes: calculating fractional lines of the candidate address; wherein the determining a final address from the plurality of candidate addresses according to the composite score of the plurality of candidate addresses comprises: determining a final address from the plurality of candidate addresses according to the composite score and the score line of the plurality of candidate addresses.
In an embodiment of the present application, the calculating the fractional lines of the candidate addresses includes: calculating the number of the demand addresses; and calculating the fraction lines of the candidate address according to the number of the demand addresses.
In an embodiment of the application, the influencing factors comprise one or more of the following classes of combinations of the influencing factors: economic factors, coordination factors and demand factors.
According to another aspect of the present application, an addressing device provided in an embodiment of the present application includes: an influence factor determination module configured to determine a value of a plurality of influence factors for each candidate address; an influence factor weight calculation module configured to calculate weights of a plurality of influence factors corresponding to the plurality of influence factors of each candidate address; an influence factor score calculation module configured to calculate a score of a plurality of influence factors of each of the candidate addresses according to the numerical values of the plurality of influence factors and the weights of the plurality of influence factors of each of the candidate addresses; a comprehensive score calculation module configured to calculate a comprehensive score of each of the candidate addresses according to scores of a plurality of influence factors of each of the candidate addresses; and the final address determining module is configured to determine a final address from the candidate addresses according to the comprehensive scores of the candidate addresses.
According to another aspect of the present application, an embodiment of the present application provides an electronic device, including: a processor; and a memory having stored therein computer program instructions which, when executed by the processor, cause the processor to perform the addressing method as set out in any preceding claim.
According to another aspect of the present application, an embodiment of the present application provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to execute the addressing method as described in any one of the preceding claims.
According to the addressing method and the addressing device, as well as the electronic equipment and the computer readable storage medium, the score of the plurality of influence factors of each candidate address is calculated according to the numerical values of the plurality of influence factors of each candidate address and the weights of the plurality of influence factors, the comprehensive score of each candidate address is calculated according to the score of the plurality of influence factors of each candidate address, and finally the final address is determined from the plurality of candidate addresses according to the comprehensive scores of the plurality of candidate addresses. The final address is selected by referring to a plurality of influence factors influencing the address selection result, the weight value of each influence factor is calculated, the final address is determined from a plurality of candidate addresses according to the comprehensive score, the address selection process has a large amount of support of data such as the influence factors and the weights, and the data such as the influence factors and the weights are subjected to quantitative processing such as calculation, so that the rationality of the final address selection is improved.
Drawings
Fig. 1 is a schematic flow chart illustrating an address selection method according to an embodiment of the present application.
Fig. 2 is a schematic flow chart illustrating an address selection method according to another embodiment of the present application.
Fig. 3 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application.
Fig. 4 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application.
Fig. 5 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application.
Fig. 6 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application.
Fig. 7 is a schematic structural diagram of an addressing device according to an embodiment of the present application.
Fig. 8 is a schematic structural diagram of an addressing device according to another embodiment of the present application.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a schematic flow chart illustrating an address selection method according to an embodiment of the present application. As shown in fig. 1, the address selecting method includes the following steps:
step 101: the values of a plurality of influencing factors for each candidate address are determined.
Specifically, a plurality of candidate addresses planned to be constructed may be provided in one area, for example, in a wuhan flood mountain area, a densely populated area such as a residential area, a university, a shopping mall, a hospital, a scenic spot, a subway station, a bus parking lot, and the like may be used as a construction area of the candidate addresses. The influencing factor may be a factor that influences the rationality of candidate address selection. The values of the influencing factors may be numerically represented.
In an embodiment of the present application, the influencing factor may be a combination of one or more of the following kinds of influencing factors: economic factors, coordination factors and demand factors. For example, the influencing factor may be only the economic factor, or may be a combination of the economic factor and the coordinating factor, or may be a combination of the economic factor, the coordinating factor, and the demand factor. It should be understood that the influencing factor may also be other kinds of influencing factors, and the influencing factor may also be a combination of other kinds of influencing factors, and the application does not specifically limit the kinds and combination forms of the influencing factors included in the influencing factors. The value of the influencing factor may be obtained according to an actual situation, or may be calculated according to specific data, for example, according to an actual situation, the value of the economic factor of one candidate address (e.g., candidate address P) may be 60, the value of the coordination factor may be 90, and the value of the demand factor may be 80.
Step 102: and calculating the weight of a plurality of influence factors corresponding to the plurality of influence factors of each candidate address.
Specifically, the weight of the influencing factor may be calculated by judging the matrix. The decision matrix may be a ═ aij) Wherein i ═ is (1,2, …, n), and j ═ is (1,2, …, n), where a isijRepresents the relative importance of the i element to the j element, and aij>0,aij＝1/aji，aii＝1，ajj1. The numerical values of the elements in the judgment matrix are respectively represented by 1, 3, 5, 7 and 9, i is equally important, slightly important, obviously important, strongly important and extremely important relative to j, and 2, 4, 6 and 8 are median values of adjacent judgments. For example, a decision matrix of the influencing factors of one candidate address P is denoted by C. Setting the demand factors,Economic and coordination factors are equally important, where C11Representing the relative importance of the demand factor and the demand factor, C12Representing the relative importance of demand and economic factors, C13Representing the relative importance of demand and coordination factors, C21Representing the relative importance of economic and demand factors, C22Representing economic factors and the relative importance of economic factors, C23Representing the relative importance of economic and coordination factors, C31Representing the relative importance of the co-ordination and demand factors, C32Representing the relative importance of the co-ordination and economic factors, C33Indicates the coordination factor and the relative importance of the coordination factor, and thus, determines element C of the matrix11、C12、C13、C14、C21、C22、C23、C31、C32And C33All values of (1). The judgment matrix C of the influence factors of the candidate address P is as follows:
Figure BDA0003150732100000051
weight W of demand factorc1The calculation formula of (2) is as follows:
Figure BDA0003150732100000052
weight W of economic factorc2The calculation formula of (2) is as follows:
Figure BDA0003150732100000053
weight W of the co-ordination factorc3The calculation formula of (2) is as follows:
Figure BDA0003150732100000061
in addition, each of the judgment matrices C due to the influence factorAll the elements are set by personnel according to actual conditions, and logic errors can occur in the setting process, for example, the setting requirement factor is slightly more important than the economic factor, the economic factor is slightly more important than the coordination factor, and the coordination factor is slightly more important than the requirement factor, so that the logic errors occur in the setting process. Therefore, it is necessary to verify whether the setting is correct. The test formula is as follows: CR is CI/RI, and CI is (lambda)MAX-n)/(n-1)，λMAXIn order to judge the maximum characteristic root of the matrix, n is the dimension of the judgment matrix, CR is the consistency ratio, RI is the random consistency index, and when n is 1-12, RI corresponds to: 0,0,0.52,0.89,1.12,1.26,1.36,1.41,1.46,1.49,1.52,1.54. If CR is<0.1, judging that the matrix meets the consistency test, namely setting correctly; otherwise, setting an error, and adjusting the judgment matrix. For example, the maximum characteristic root λ of the determination matrix C of the above-mentioned influencing factorsMAXThe number of dimensions n of the judgment matrix is 3, and therefore, the calculation result of the test formula is 0, that is, the judgment matrix C of the influencing factors satisfies the consistency test and is set correctly.
Step 103: and calculating the scores of the plurality of influence factors of each candidate address according to the numerical values of the plurality of influence factors of each candidate address and the weights of the plurality of influence factors.
Specifically, the score of the influence factor may be obtained by multiplying the value of the influence factor by the weight of the corresponding influence factor. For example, the number of demand factors of the influencing factors of the candidate address P is 80, the number of economic factors is 60, the number of coordination factors is 90, the weights of the demand factors, the economic factors and the coordination factors are all 0.33, the score of the demand factors is 80 × 0.33 — 26.4, the score of the economic factors is 60 × 0.33 — 19.8, and the score of the coordination factors is 90 × 0.33 — 29.7.
Step 104: and calculating the comprehensive score of each candidate address according to the scores of the plurality of influence factors of each candidate address.
Specifically, the scores of the multiple influencing factors of each candidate address may be added to obtain a composite score of each candidate address. For example, taking the example of step 103 as an example, the total score of the candidate addresses P is 26.4+19.8+ 29.7-75.9 points.
Step 105: and determining a final address from the plurality of candidate addresses according to the comprehensive scores of the plurality of candidate addresses.
Specifically, the method of steps 101 to 104 may be used to calculate a plurality of composite scores for a plurality of candidate addresses, and then determine a final address from the plurality of candidate addresses according to the plurality of composite scores for the plurality of candidate addresses. For example, a candidate address having a composite score exceeding 60 points may be selected as the final address, or a plurality of composite scores of a plurality of candidate addresses may be ranked from high to low, and the top 50 candidate addresses may be selected as the final addresses. It should be understood that the present application is not particularly limited to the manner of determining the final address from the plurality of candidate addresses according to the plurality of composite scores of the plurality of candidate addresses.
Therefore, according to the addressing method provided by the embodiment of the application, the score of the plurality of influence factors of each candidate address is calculated according to the numerical values of the plurality of influence factors of each candidate address and the weights of the plurality of influence factors, the comprehensive score of each candidate address is calculated according to the score of the plurality of influence factors of each candidate address, and finally the final address is determined from the plurality of candidate addresses according to the comprehensive scores of the plurality of candidate addresses. The final address is selected by referring to a plurality of influence factors influencing the address selection result, the weight value of each influence factor is calculated, the final address is determined from a plurality of candidate addresses according to the comprehensive score, the address selection process has a large amount of support of data such as the influence factors and the weights, and the data such as the influence factors and the weights are subjected to quantitative processing such as calculation, so that the rationality of the final address selection is improved.
Fig. 2 is a schematic flow chart illustrating an address selection method according to another embodiment of the present application. As shown in fig. 2, determining the values of the plurality of influencing factors for each candidate address comprises the following steps:
step 201: the values of the plurality of sub-contributors to each contributor are determined.
Specifically, each influence factor may include a plurality of sub-influence factors. For example, demand factors may include a crowd density sub-factor and a travel density sub-factor; economic factors may include a vehicle cost sub-factor, a land cost sub-factor, a management cost sub-factor, and a technical cost sub-factor. The coordination factors may include a public transportation sub-factor and a resource repetition sub-factor. The value of each sub-influence factor may be obtained according to an actual situation, or may be calculated according to specific data, for example, according to an actual situation, the value of the crowd density sub-factor of the demand factor of one candidate address (for example, candidate address P) may be 60, and the value of the travel density sub-factor may be 90; the vehicle cost sub-factor for the economic factor may have a value of 80, the land cost sub-factor may have a value of 75, the administrative cost sub-factor may have a value of 83, and the technical cost sub-factor may have a value of 69; the value of the public transportation sub-factor of the coordination factor may be 92, and the value of the resource repetition sub-factor may be 76, and the present application does not specifically limit the magnitude or the calculation manner of the values of the plurality of sub-influence factors of each influence factor.
Step 202: determining the values of the plurality of influencing factors for each candidate address based on the values of the plurality of sub-influencing factors for each influencing factor.
Specifically, the numerical values of the multiple sub-influence factors of each influence factor may be added to obtain the numerical value of each influence factor, or the numerical values of the multiple sub-influence factors of each influence factor may be averaged to obtain the numerical value of each influence factor. For example, taking the example of step 201 as an example, the value of the demand factor of the candidate address P may be the sum of the value 60 of the crowd density sub-factor and the value 90 of the travel density sub-factor, i.e. 150, or the average value of the value 60 of the crowd density sub-factor and the value 90 of the travel density sub-factor, i.e. 75.
The value of the influence factor is obtained through calculation according to the values of the sub-influence factors, rather than only selecting the value of the influence factor according to the experience of personnel, and the rationality of the final address selection is further improved.
Fig. 3 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application. As shown in fig. 3, determining the values of the plurality of influence factors for each candidate address according to the values of the plurality of sub-influence factors for each influence factor comprises the following steps:
step 301: and calculating the weights of the plurality of sub-influence factors corresponding to the plurality of sub-influence factors of each influence factor.
Specifically, the weights of the sub-influence factors may be calculated by judging the matrix. The decision matrix may be a ═ aij) Wherein i ═ is (1,2, …, n), and j ═ is (1,2, …, n), where a isijRepresents the relative importance of the i element to the j element, and aij>0,aij＝1/aji，aii＝1，ajj1. The numerical values of the elements in the judgment matrix are respectively represented by 1, 3, 5, 7 and 9, i is equally important, slightly important, obviously important, strongly important and extremely important relative to j, and 2, 4, 6 and 8 are median values of adjacent judgments. For example, C is used as the judgment matrix of the sub-influence factors of the demand factors of one candidate address P1And (4) showing. Set population Density sub-factor D1Specific travel density sub-factor D2Of slight importance, wherein D11Representing the relative importance of the sub-factor of population density and the sub-factor of population density, D12Representing the relative importance of the sub-factors of population density and travel density, D21Representing the relative importance of the travel and population density sub-factors, D22Representing the relative importance of the travel density sub-factor and the travel density sub-factor, and thus, determining element D of the matrix11＝1，D12＝3，
Figure BDA0003150732100000091
D221. Decision matrix C of influencing factors of candidate address P1Comprises the following steps:
Figure BDA0003150732100000092
weight W of crowd density sub-factorD1The calculation formula of (2) is as follows:
Figure BDA0003150732100000093
weight W of travel density sub-factorD2The calculation formula of (2) is as follows:
Figure BDA0003150732100000094
in addition, since each element of the judgment matrix of the sub-influence factors is set by a person according to the actual situation, a logic error may occur in the setting process. Therefore, it is necessary to verify whether the setting is correct. The test formula is as follows: CR is CI/RI, and CI is (lambda)MAX-n)/(n-1)，λMAXIn order to judge the maximum characteristic root of the matrix, n is the dimension of the judgment matrix, CR is the consistency ratio, RI is the random consistency index, and when n is 1-12, RI corresponds to: 0,0,0.52,0.89,1.12,1.26,1.36,1.41,1.46,1.49,1.52,1.54. If CR is<0.1, judging that the matrix meets the consistency test, namely setting correctly; otherwise, setting an error, and adjusting the judgment matrix. For example, the judgment matrix C of the above-mentioned demand factors1Maximum characteristic root λ ofMAXIs 2, judge the matrix C1Is 2, the calculation result of the test formula is 0, i.e. the judgment matrix C of the demand factor1The consistency check is satisfied and the setting is correct.
Similarly, C is used for judging matrix of sub-influence factors of economic factors of candidate address P2And (4) showing. Setting the importance of the land cost sub-factor to the vehicle cost sub-factor between equally and slightly important, i.e. E21Representing the importance of the land cost sub-factor and the vehicle cost sub-factor, E21＝2，
Figure BDA0003150732100000101
Similarly, a determination matrix C of sub-influence factors of economic factors of the candidate address P can be set2Comprises the following steps:
Figure BDA0003150732100000102
similarly, the weight W of the vehicle cost sub-factorE1Calculating to obtain the weight W of 0.120, a land cost sub-factorE2Calculated to be 0.171, the weight W of the administrative cost sub-factorE3The weight W of the technical cost sub-factor is calculated to be 0.260E4Calculated to be 0.450. The judgment matrix C of the economic factors2Maximum characteristic root λ ofMAXIs 4.07, judge matrix C2Is 4, and when n is 4, RI corresponds to 0.89, and therefore, the calculation result of the test formula is CI ═ λ (λ)MAX-n)/(n-1) ═ (4.07-4)/(4-1) ═ 0.0233, so CR ═ CI/RI ═ 0.0233/0.89 ═ 0.026<0.1, judgment matrix C of demand factor2The consistency check is satisfied and the setting is correct.
Similarly, C is used for the judgment matrix of the sub-influence factors of the coordination factors of the candidate address P3And (4) showing. Determining matrix C for setting sub-influence factors of coordination factors of candidate address P3Comprises the following steps:
Figure BDA0003150732100000103
similarly, the weight W of the public transportation sub-factorF1Calculating to obtain the weight W of the resource repetition sub-factor of 0.5F2Calculated to be 0.5. Similarly, the judgment matrix C of the coordination factors is verified3The consistency check is satisfied and the setting is correct.
Step 302: and calculating the numerical values of the plurality of influence factors of each candidate address according to the numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
Specifically, the value of each sub-influence factor and the weight of the corresponding sub-influence factor may be multiplied to obtain a product, and the products of the plurality of sub-influence factors of each influence factor are added to obtain the value of each influence factor. For example, in the example of step 301, it is assumed that the value of the population density sub-factor of the demand factor of the candidate address P is 60, and the value of the travel density sub-factor is 90; the vehicle cost sub-factor for the economic factor has a value of 80, the land cost sub-factor has a value of 75, the management cost sub-factor has a value of 83, and the technical cost sub-factor has a value of 69; the value of the mass transport sub-factor of the co-ordination factor is 92 and the value of the resource repetition sub-factor is 76.
Thus, the values of the demand factors may be: 60 × 0.75+90 × 0.25 ═ 67.5; the value of the economic factor may be: 80 × 0.120+75 × 0.171+83 × 0.260+69 × 0.450 ═ 75.055; the values of the co-ordination factors may be: 92 × 0.5+76 × 0.5 ═ 84.
The numerical values of the multiple influence factors of each candidate address are calculated according to the numerical values of the multiple sub-influence factors of each influence factor and the weights of the corresponding multiple sub-influence factors, instead of simply obtaining the numerical values of the multiple influence factors according to calculation modes such as average values or summation of the numerical values of the sub-influence factors, and the rationality of address selection is further improved.
Fig. 4 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application. As shown in fig. 4, calculating the values of the plurality of influencing factors for each candidate address according to the values of the plurality of sub-influencing factors for each influencing factor and the weights of the corresponding plurality of sub-influencing factors includes the following steps:
step 401: and normalizing the numerical values of the plurality of sub-influence factors of each influence factor to obtain the normalized numerical value of the plurality of sub-influence factors of each influence factor.
Specifically, the normalization processing is performed by taking the candidate address P in the above embodiment as an example. Suppose that the sample data for 350 candidate addresses in the region of candidate address P is as follows:
numerical value of population density sub-factor (people/square kilometer): a (a1, a2, a3... a 350);
values of trip density sub-factors: b (b1, b2, b 3.. b 350);
value of vehicle cost sub-factor: c (c1, c2, c3... c 350);
numerical value of land cost sub-factor: d (d1, d2, d3... d 350);
numerical values of the management cost sub-factors (monthly): e (e1, e2, e3... e 350);
numerical values of technical cost sub-factors: f (f1, f2, f3... f 350);
numerical values of the public transportation sub-factors (number of buses and subways within 1 km near a stop): g (g1, g2, g3... g 350);
numerical value of resource repetition sub-factor (number of shared car sites within 1 km around a site): h (h1, h2, h3... h 350).
The sub-influence factors are divided into positive sub-influence factors and negative sub-influence factors. The positive sub-influence factors can increase the number of the final addresses, and the negative sub-influence factors can decrease the number of the final addresses. For example, the crowd density sub-factor and the travel density sub-factor are positive sub-influence factors, and the vehicle cost sub-factor, the land cost sub-factor, the management cost sub-factor, the technical cost sub-factor, the public transportation sub-factor, and the resource repetition sub-factor are negative sub-influence factors.
The positive sub-influence factor normalization formula is:
Figure BDA0003150732100000121
wherein, axIs the value of the positive sub-influence factor, a ', of any one of the candidate addresses'xIs the normalized value of the positive sub-factor of any one of the candidate addresses.
For example, the value of the population density sub-factor of the candidate address P is apThe standardized value of the population density sub-factor is a'pThen, then
Figure BDA0003150732100000122
Similarly, the value of the sub-factor of the travel density is bpThe standardized value of the population density sub-factor is b'pThen, then
Figure BDA0003150732100000123
The negative sub-influence factor normalization formula is as follows:
Figure BDA0003150732100000124
wherein, cxIs the value of the negative sub-influencer for any one of the candidate addresses, c'xIs a normalized value of the negative going sub-contributor for any one of the candidate addresses.
For example, the value c of the vehicle cost sub-factor for the candidate address PpThe standardized value of the population density sub-factor is c'pThen, then
Figure BDA0003150732100000131
Similarly, the sub-factor of land cost has a value of dpD 'is the normalized value of the land cost sub-factor'pThen, then
Figure BDA0003150732100000132
Similarly, the value of the administrative cost sub-factor is epThe standardized value of the land cost sub-factor is e'pThen, then
Figure BDA0003150732100000133
Similarly, the numerical value of the technical cost sub-factor is fpThe normalized value of the technical cost sub-factor is fp', then
Figure BDA0003150732100000134
Similarly, the value of the public transportation sub-factor is gpThe standardized value of the public transportation sub-factor is g'pThen, then
Figure BDA0003150732100000135
Similarly, the value of the resource repetition sub-factor is hpThe normalized value of the resource repetition sub-factor is h'pThen, then
Figure BDA0003150732100000141
Step 402: and calculating the numerical values of the plurality of influence factors of each candidate address according to the standardized numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
Specifically, the normalized value of each sub-influence factor may be multiplied by the weight of the corresponding sub-influence factor to obtain a product, and the products of the plurality of sub-influence factors of each influence factor may be added to obtain the value of each influence factor. The values of the demand factors may be: wD1a'p+WD2b'p(ii) a The value of the economic factor may be: wE1c'p+WE2d'p+WE3e'p+WE4fp'; the values of the co-ordination factors may be: wF1g'p+WF2h'p。
By standardizing the values of the sub-influence factors, the values of the sub-influence factors have a uniform standard, and the rationality of the final address selection is further improved.
Fig. 5 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application. As shown in fig. 5, the method further comprises the steps of:
step 501: fractional lines of the candidate address are calculated.
Specifically, the score line of the candidate address may be an average value of a plurality of comprehensive scores of the plurality of candidate addresses, or may also be a median value of a plurality of comprehensive scores of the plurality of candidate addresses.
According to the comprehensive scores of the candidate addresses, the step of determining the final address from the candidate addresses comprises the following steps:
step 502: and determining a final address from the plurality of candidate addresses according to the comprehensive scores and the score lines of the plurality of candidate addresses.
Specifically, the comprehensive score of the candidate address may be compared with the fractional line, and the candidate address with the comprehensive score larger than the fractional line is obtained as the final address.
By setting the fractional lines, the selection of the final address can be selected according to the fractional lines, and the rationality of the selection of the final address is further improved.
Fig. 6 is a schematic flowchart illustrating an address selecting method according to another embodiment of the present application. As shown in fig. 6, calculating fractional lines for a plurality of candidate addresses includes the steps of:
step 601: the number of demand addresses is calculated.
Specifically, the number of demand addresses may be determined according to the past demand number, or may be obtained by calculation according to actual data, and specifically may be calculated by the following formula:
Figure BDA0003150732100000151
the method comprises the following steps that A represents the number of required addresses of a certain area, N represents the total population of the area, Q represents the daily trip mileage of people in the area, S represents the proportion of using shared automobiles or shared bicycles in a trip mode, V represents the average running speed of the shared automobiles or the shared bicycles, T is the daily average running time of each shared automobile or shared bicycle, phi is the normal running time proportion of the shared automobiles or shared bicycles, and B is the average number of vehicles of each shared automobile or shared bicycle station.
Step 602: and calculating the fraction lines of the candidate addresses according to the number of the demand addresses.
Specifically, the score line may be calculated using the following formula:
Figure BDA0003150732100000152
wherein, T represents a score line, O represents the lowest comprehensive score in the comprehensive scores of all the candidate addresses, I represents the highest comprehensive score in the comprehensive scores of all the candidate addresses, M represents the number of all the candidate addresses, and A represents the number of the demand addresses of a certain area.
Since the shared automobile or the shared bicycle is a public transportation means, in order to ensure profitability and convenience of use for residents, the supply quantity is balanced with the demand quantity, and for example, it is the optimal supply scheme that the supply quantity is equal to the demand quantity. Therefore, the number of the determined final addresses is close to the number of the demand addresses as much as possible by calculating the fraction lines by using the number of the demand addresses of a certain area so as to determine the final addresses in a plurality of candidate addresses, and the rationality of the selection of the final addresses is further improved.
Fig. 7 is a schematic structural diagram of an addressing device according to an embodiment of the present application. As shown in fig. 7, the addressing device 70 includes: an influence factor determination module 701, an influence factor weight calculation module 702, an influence factor score calculation module 703, a composite score calculation module 704, and a final address determination module 705. The influencing factor determination module 701 is configured to: the values of a plurality of influencing factors for each candidate address are determined. The influencer weight calculation module 702 is configured to: and calculating the weight of a plurality of influence factors corresponding to the plurality of influence factors of each candidate address. The influencer score calculation module 703 is configured to: and calculating the scores of the plurality of influence factors of each candidate address according to the numerical values of the plurality of influence factors of each candidate address and the weights of the plurality of influence factors. The composite score calculation module 704 is configured to: and calculating the comprehensive score of each candidate address according to the scores of the plurality of influence factors of each candidate address. The final address determination module 705 is configured to: and determining a final address from the plurality of candidate addresses according to the comprehensive scores of the plurality of candidate addresses.
Fig. 8 is a schematic structural diagram of an addressing device according to another embodiment of the present application. As shown in fig. 8, the influencing factor determining module 701 includes: a sub influence factor determination sub-module 801 and an influence factor determination sub-module 802. The sub-influence factor determination sub-module 801 is configured to: the values of the plurality of sub-contributors to each contributor are determined. The influencing factor determination submodule 802 is configured to: determining the values of the plurality of influencing factors for each candidate address based on the values of the plurality of sub-influencing factors for each influencing factor.
The influence factor determination sub-module 802 includes: a sub-influence factor weight calculation unit 8021 and an influence factor numerical value calculation unit 8022. The sub-influence factor weight calculation unit 8021 is configured to: and calculating the weights of the plurality of sub-influence factors corresponding to the plurality of sub-influence factors of each influence factor. The influence factor numerical value calculation unit 8022 is configured to: and calculating the numerical values of the plurality of influence factors of each candidate address according to the numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
The influence factor numerical value calculation unit 8022 includes: a sub-influence factor normalization subunit 80221 and an influence factor numerical calculation subunit 80222. The sub-influencer normalization subunit 80221 is configured as: and normalizing the numerical values of the plurality of sub-influence factors of each influence factor to obtain the normalized numerical value of the plurality of sub-influence factors of each influence factor. The influence factor numerical calculation subunit 80222 is configured to: and calculating the numerical values of the plurality of influence factors of each candidate address according to the standardized numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
The addressing device 70 further comprises: a score line calculation module 706. The score line calculation module 706 is configured to: fractional lines of the candidate address are calculated.
The final address determination module 705 is further configured to: and determining a final address from the plurality of candidate addresses according to the comprehensive scores and the score lines of the plurality of candidate addresses.
The score line calculation module 706 includes: a demand address number calculation submodule 7061 and a fractional line calculation submodule 7062. The demand address number calculation submodule 7061 is configured to: the number of demand addresses is calculated. Fractional line calculation submodule 7062 is configured to: and calculating the fraction lines of the candidate addresses according to the number of the demand addresses.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 9, the electronic apparatus 90 includes: one or more processors 901 and memory 902; and computer program instructions stored in the memory 902, which, when executed by the processor 901, cause the processor 901 to perform the addressing method of any of the embodiments described above.
The processor 901 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 90 to perform desired functions.
Memory 902 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer-readable storage medium and executed by processor 901 to implement the steps in the addressing methods of the various embodiments of the present application described above and/or other desired functions. Information such as alternative addresses, cloud maps, various common map information, geographic knowledge, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 90 may further include: an input device 903 and an output device 904, which are interconnected by a bus system and/or other form of connection mechanism (not shown in fig. 9).
For example, when the electronic device 90 is a stand-alone device, the input device 903 may be a communication network connector for receiving the collected input signal from an external removable device. The input device 903 may also include, for example, a keyboard, a mouse, a microphone, and the like.
The output device 904 may output various information to the outside, and may include, for example, a display, a speaker, a printer, and a communication network and a remote output apparatus connected thereto, and so on.
Of course, for simplicity, only some of the components of the electronic device 90 relevant to the present application are shown in fig. 9, and components such as a bus, an input device/output interface, and the like are omitted. In addition, the electronic device 90 may include any other suitable components, depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the addressing method of any of the above-described embodiments.
The computer program product may include program code for carrying out operations for embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in the addressing method of the various embodiments of the present application.
A computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory ((RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of the devices and apparatuses referred to in this application are only given as illustrative examples and are not intended to require or imply that the devices and apparatuses must be connected, arranged, or configured in the manner shown in the block diagrams. These devices and apparatuses may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modifications, equivalents and the like that are within the spirit and principle of the present application should be included in the scope of the present application.

Claims (10)

1. An addressing method, comprising:
determining the numerical values of a plurality of influence factors of each candidate address;
calculating the weight of a plurality of influence factors corresponding to the plurality of influence factors of each candidate address;
calculating scores of the plurality of influence factors of each candidate address according to the numerical values of the plurality of influence factors of each candidate address and the weights of the plurality of influence factors;
calculating a composite score of each candidate address according to the scores of the plurality of influence factors of each candidate address; and
and determining a final address from the candidate addresses according to the comprehensive scores of the candidate addresses.
2. The addressing method of claim 1, wherein determining the values of the plurality of influencing factors for each candidate address comprises:
determining a number of each of a plurality of sub-factors of influence; and
determining a number of said influential values for each said candidate address based on a number of said sub-influential values for each said influential.
3. The addressing method of claim 2, wherein said determining values for a plurality of said influencers for each of said candidate addresses based on values for a plurality of said sub-influencers for each of said influencers comprises:
calculating the weight of a plurality of sub-influence factors corresponding to a plurality of sub-influence factors of each influence factor; and
and calculating the numerical values of the plurality of influence factors of each candidate address according to the numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
4. The addressing method of claim 3, wherein said calculating the values of the plurality of influencers for each of the candidate addresses based on the values of the plurality of sub-influencers for each of the influencers and the weights of the corresponding plurality of sub-influencers comprises:
normalizing the numerical values of the multiple sub-influence factors of each influence factor to obtain normalized numerical values of the multiple sub-influence factors of each influence factor; and
and calculating the numerical values of the plurality of influence factors of each candidate address according to the standardized numerical values of the plurality of sub-influence factors of each influence factor and the weights of the corresponding plurality of sub-influence factors.
5. The addressing method of claim 1, further comprising:
calculating fractional lines of the candidate address;
wherein the determining a final address from the plurality of candidate addresses according to the composite score of the plurality of candidate addresses comprises:
determining a final address from the plurality of candidate addresses according to the composite score and the score line of the plurality of candidate addresses.
6. The addressing method of claim 5, wherein said computing fractional lines of a plurality of said candidate addresses comprises:
calculating the number of the demand addresses; and
and calculating the fraction lines of the candidate address according to the number of the demand addresses.
7. The addressing method according to claim 1, wherein said influencing factors comprise one or more of the following classes of said influencing factors in combination: economic factors, coordination factors and demand factors.
8. An addressing device, comprising:
an influence factor determination module configured to determine a value of a plurality of influence factors for each candidate address;
an influence factor weight calculation module configured to calculate weights of a plurality of influence factors corresponding to the plurality of influence factors of each candidate address;
an influence factor score calculation module configured to calculate a score of a plurality of influence factors of each of the candidate addresses according to the numerical values of the plurality of influence factors and the weights of the plurality of influence factors of each of the candidate addresses;
a comprehensive score calculation module configured to calculate a comprehensive score of each of the candidate addresses according to scores of a plurality of influence factors of each of the candidate addresses; and
and the final address determining module is configured to determine a final address from the candidate addresses according to the comprehensive scores of the candidate addresses.
9. An electronic device, comprising:
a processor; and
memory having stored therein computer program instructions which, when executed by the processor, cause the processor to perform the addressing method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the addressing method of any of claims 1 to 7.
CN202110764906.0A 2021-07-06 2021-07-06 Site selection method and site selection device Pending CN113657709A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110764906.0A CN113657709A (en) 2021-07-06 2021-07-06 Site selection method and site selection device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110764906.0A CN113657709A (en) 2021-07-06 2021-07-06 Site selection method and site selection device

Publications (1)

Publication Number Publication Date
CN113657709A true CN113657709A (en) 2021-11-16

Family

ID=78477156

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110764906.0A Pending CN113657709A (en) 2021-07-06 2021-07-06 Site selection method and site selection device

Country Status (1)

Country Link
CN (1) CN113657709A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106570595A (en) * 2016-11-10 2017-04-19 深圳市数字城市工程研究中心 Subway station position selection method and system based on space big data
CN108154300A (en) * 2017-12-25 2018-06-12 东软集团股份有限公司 Point of interest site selecting method, device and computer equipment
CN110956802A (en) * 2019-11-08 2020-04-03 桂林电子科技大学 Road side unit site selection method and system based on road buffer area superposition analysis
CN112101700A (en) * 2020-07-24 2020-12-18 国网上海市电力公司 Evaluation method of multi-station fusion site selection index system
CN112231923A (en) * 2020-10-23 2021-01-15 国网山东省电力公司济南供电公司 Site selection method for 'multi-station integration' data center station

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106570595A (en) * 2016-11-10 2017-04-19 深圳市数字城市工程研究中心 Subway station position selection method and system based on space big data
CN108154300A (en) * 2017-12-25 2018-06-12 东软集团股份有限公司 Point of interest site selecting method, device and computer equipment
CN110956802A (en) * 2019-11-08 2020-04-03 桂林电子科技大学 Road side unit site selection method and system based on road buffer area superposition analysis
CN112101700A (en) * 2020-07-24 2020-12-18 国网上海市电力公司 Evaluation method of multi-station fusion site selection index system
CN112231923A (en) * 2020-10-23 2021-01-15 国网山东省电力公司济南供电公司 Site selection method for 'multi-station integration' data center station

Similar Documents

Publication Publication Date Title
CN108961087B (en) Insurance recommendation method and device, computer equipment and computer-readable storage medium
CN110288137B (en) Road network level road infrastructure structure comprehensive maintenance decision method and system
CN112566196A (en) Heterogeneous network access selection method based on smart power grid and related equipment
Hou et al. Novel and practical method to quantify the quality of mobility: Mobility energy productivity metric
US20170300991A1 (en) Used-vehicle algorithmic pricing engine method and system
US20120066004A1 (en) Method and system for personal insurance comparison and advice
Matalqah et al. Impact of different penetration rates of shared autonomous vehicles on traffic: Case study of Budapest
CN113222326A (en) Method and device for evaluating maturity of scientific and technological resource service platform
CN115668265A (en) Risk transfer configurator and simulation engine providing look-ahead and look-behind metrics for risk driven combinations of manipulating and adjusting underwriting objectives and method thereof
CN114580847A (en) Energy efficiency assessment service resource supply and demand matching system and method considering similarity and credibility
CN113888312A (en) Method, device, electronic equipment and medium for determining business quota
CN108564396A (en) Multimode traffic trip questionnaire survey design method based on D-error design effectivelies
CN113657709A (en) Site selection method and site selection device
CN111401787A (en) Site selection method, device and system
CN111859289A (en) Transaction conversion rate estimation method and device for vehicle, electronic device and medium
CN114724414B (en) Method and device for determining urban air traffic sharing rate, electronic equipment and medium
Bilali et al. Analytical model to estimate ride pooling traffic impacts by using the macroscopic fundamental diagram
CN110826943A (en) Method and related equipment for judging whether bus allocation is needed or not and determining bus allocation number
CN114022045A (en) Regional economic development level determining method and device and terminal equipment
CN111833595B (en) Shared automobile auxiliary vehicle configuration method, electronic device and storage medium
CN115269863A (en) Credible evaluation method and system for network knowledge in traffic field
CN107743074A (en) A kind of degree of belief evaluation method of composite services
CN114330888A (en) Order processing method and system and electronic equipment
CN112070354A (en) Power grid equipment operation efficiency evaluation method
WO2013009604A2 (en) System and method for risk validation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination