CN117391861B - Low-carbon point accounting transaction method and system based on cloud platform - Google Patents

Low-carbon point accounting transaction method and system based on cloud platform Download PDF

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CN117391861B
CN117391861B CN202311685289.0A CN202311685289A CN117391861B CN 117391861 B CN117391861 B CN 117391861B CN 202311685289 A CN202311685289 A CN 202311685289A CN 117391861 B CN117391861 B CN 117391861B
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CN117391861A (en
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景国胜
马小毅
何健
卞芸芸
刘玮
赵国锋
方舟
卢德伟
刘婷
钟瑞锋
肖伟智
谢加红
林俊琦
张磊
伍敏冬
李帅帅
谭坚欣
沈建萍
李璧君
李姿莹
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Guangzhou Transportation Planning And Research Institute Co ltd
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Abstract

The invention provides a low-carbon point accounting transaction method and system based on a cloud platform, and relates to the technical field of low-carbon platforms, wherein the method comprises the following steps: receiving a low carbon integrated accounting request from a client for a target low carbon behavior; acquiring first user behavior data corresponding to a user identifier from a target database corresponding to target interface information; verifying whether the first user behavior data meets a low carbon integral accounting condition corresponding to the corresponding target low carbon behavior; if the verification result is satisfied, distributing a target low-carbon integral corresponding to the target low-carbon behavior aiming at the user identification; and if the verification result is not satisfied, rejecting to allocate the target low carbon integral aiming at the user identification, and feeding back accounting failure notification to the client. Therefore, the data auditing pressure of the low-carbon platform is reduced, the operation cost of the low-carbon platform is reduced, and the stable and orderly operation of the low-carbon platform is assisted.

Description

Low-carbon point accounting transaction method and system based on cloud platform
Technical Field
The invention relates to the technical field of low-carbon platforms, in particular to a cloud platform-based low-carbon integration accounting transaction method and system.
Background
Under the guidance of carbon reaching peak, carbon neutralization and low carbon energy conservation, the whole society actively advocates green low carbon consumption and trip. Some cities actively promote low-carbon behaviors of citizens, and actively promote respective low-carbon platforms (such as carbon general electronic platforms), and calculate the carbon reduction of users according to low-carbon behavior data after citizen users implement appointed low-carbon behaviors, and convert the carbon reduction into carbon integration, so that the users can exchange corresponding commodities or offers on the platforms according to the carbon integration.
Currently, after citizen users upload evidence of related low-carbon behaviors for specific low-carbon behaviors, for example, citizen users upload screenshots of their shared bicycle riding records, a platform administrator performs data auditing, and after the data auditing, the platform automatically distributes carbon accumulation corresponding to the low-carbon behaviors to citizen users.
However, the platform lacks an audit mechanism for the authenticity of the low-carbon behavior of citizen users, resulting in some citizen users uploading screenshot evidence of counterfeit low-carbon behavior to the low-carbon platform to take carbon credits over, but it does not implement any low-carbon behavior. On the one hand, this behavior renders the low-carbon platform unable to account for the real carbon emission data, and also results in the release of a large number of goods or offers under abnormal conditions, resulting in an increase in platform operating costs. On the other hand, the behavior adds huge data auditing pressure to the low-carbon platform, and is unfavorable for the stable and orderly operation of the low-carbon platform.
In view of the above problems, currently, no preferred solution is proposed.
Disclosure of Invention
The invention provides a low-carbon integration accounting transaction method and system based on a cloud platform, which are used for at least solving the problem that in the prior art, the low-carbon platform cannot account for the authenticity of carbon emission data uploaded by a user, so that the data auditing pressure of the platform is overlarge.
The invention provides a low-carbon point accounting transaction method based on a cloud platform, which is applied to a cloud platform server, and comprises the following steps: receiving a low carbon integrated accounting request from a client for a target low carbon behavior; the low carbon point accounting request includes a user identification; under the condition that the target low-carbon behavior is determined to be matched with a first preset behavior set, calling target interface information corresponding to the target low-carbon behavior; the first preset behavior set comprises a plurality of first preset behaviors and corresponding interface information; each interface information is used for authorizing access to a database of the partner operator; acquiring first user behavior data corresponding to the user identifier from a target database corresponding to the target interface information; verifying whether the first user behavior data meets a low carbon integral accounting condition corresponding to the target low carbon behavior; if the verification result is satisfied, distributing a target low-carbon integral corresponding to the target low-carbon behavior for the user identification; and if the verification result is not satisfied, refusing to allocate the target low carbon integral aiming at the user identification, and feeding back accounting failure notification to the client.
Optionally, after receiving a low carbon integration accounting request from the client for the target low carbon behavior, the method further comprises: under the condition that the target low-carbon behavior is matched with a second preset behavior set, establishing long connection with the client, and sending an evidence collection request to the client, so that the client triggers the client to perform screen video recording operation based on the evidence collection request to generate corresponding screen video; and the client is further configured to detect a user operation during on-screen video recording to generate at least one evidence screenshot corresponding to the target low-carbon behavior; continuously receiving, from the client, respective ones of the evidence screenshots responsive to the evidence collection request based on the long connection; based on the respective evidence screenshots, it is determined whether to assign the target low-carbon score for the user identification.
Optionally, after said continuously receiving each of said evidence screenshots from said client in response to said evidence collection request based on said long connection, said method further comprises: sending a feedback notice to the client and disconnecting the long connection; the feedback notification defines a message for notifying the user that the on-screen video and the evidence screenshot have been synchronously uploaded.
Optionally, after said continuously receiving each of said evidence screenshots from said client in response to said evidence collection request based on said long connection, said method further comprises: and sending backup library interface information to the client so that the client sends the screen recording video to a backup database corresponding to the backup library interface information.
Optionally, after receiving a low carbon integration accounting request from the client for the target low carbon behavior, the method further comprises: verifying whether the client belongs to a platform self-operating client or not based on the client identification information of the client; when the client belongs to the platform self-operating client, the client sensing data corresponding to the preset time period is called according to the client identification information; the client sensing data is data acquired based on at least one sensing module of the client; determining second user behavior data matched with the client-side sensing data; verifying whether the second user behavior data satisfies the low carbon integral accounting condition.
Optionally, before verifying whether the first user behavior data meets the low carbon integration accounting condition corresponding to the target low carbon behavior, the method further includes: acquiring a low-carbon behavior set; the low carbon behavior set comprises at least one set low carbon behavior; aiming at each set low-carbon behavior in the low-carbon behavior set, calculating the low-carbon behavior rewarding proportion corresponding to the set low-carbon behavior; and determining low-carbon points corresponding to the set low-carbon behaviors respectively according to the low-carbon behavior rewarding proportion and a preset point basic value.
Optionally, the low carbon behavioral rewards specific gravity is calculated by including: preset index weight for obtaining k low-carbon behavior evaluation indexes; Setting low-carbon behaviors for M low-carbon behavior sets, constructing a low-carbon evaluation matrix I= (a ij)k×M,aij is a characteristic value of the j-th low-carbon behavior set low-carbon behavior in the I-th low-carbon behavior evaluation index) for the k low-carbon behavior evaluation indexes, and calculating a relative membership matrix O= (O ij)k×M) corresponding to the low-carbon evaluation matrix based on the low-carbon evaluation matrix I= (a ij)k×M, wherein O ij is a relative membership of the characteristic value a ij):
；
For each set low-carbon behavior, calculating the comprehensive relative membership degree H j of the total weight vector H= (v 1,v2…,vm) of the relative membership degree relative to the set low-carbon behavior j according to the index expert weight rho corresponding to the set low-carbon behavior:
；
Wherein α is an optimization criterion parameter, α=1 corresponds to a least-squares power, and α=2 corresponds to a least-squares power; d is a distance parameter, d=1 and d=2 correspond to hamming distance and euclidean distance, respectively; t is alpha, d and takes the serial numbers of the group numbers corresponding to different values;
the calculation formula for calculating the low-carbon behavior rewarding proportion gamma i∈{γ1,γ2…,γM},γi corresponding to each set low-carbon behavior is as follows:
；
wherein T is the total number of groups of values of alpha and d, an 。
Optionally, the obtaining preset index weights of the k low-carbon behavior evaluation indexes includes: acquiring a weight expert sample set S= { S 1,s2,…,sm } aiming at the k low-carbon behavior evaluation indexes U= { U 1,u2,…,uk }; the weight expert sample set comprises a plurality of weight expert samples, each weight expert sample corresponds to a unique environmental protection technical expert, and the weight expert sample comprises an index weight proposal value B= { B 1,b2,…,bk } of the environmental protection technical expert for each low-carbon behavior evaluation index; based on an enhanced analytic hierarchy process and the weight expert sample set, determining the index weight corresponding to each low-carbon behavior evaluation index specifically comprises: based on the weight expert sample set, weight suggestion values of all environmental protection technical experts about low-carbon behavior evaluation index combinations are screened to construct corresponding paired comparison matrixes Q (n) =; Wherein the low carbon behavior evaluation index combination comprises low carbon behavior evaluation indexes u x and u y for comparison; n represents the total number of combinations corresponding to the low-carbon behavior evaluation index combinations determined based on the k low-carbon behavior evaluation indexes;
And counting the paired comparison matrixes corresponding to the low-carbon behavior evaluation index combinations to calculate corresponding total standard deviation sigma xy:
；
When sigma xy is more than or equal to 1, generating a sample set replacement notification for the weight expert sample set;
And when sigma xy is less than 1, counting the average value of index weight recommended values of the low-carbon behavior evaluation indexes in the weight expert samples according to the low-carbon behavior evaluation indexes, and determining the index weight corresponding to the low-carbon behavior evaluation indexes according to the average value.
Optionally, the low-carbon performance evaluation index includes at least one of: low carbon emissions, public opinion attention and economic impact.
The application also provides a low-carbon point accounting transaction system based on the cloud platform, which comprises the following steps: a receiving unit configured to receive a low carbon integration accounting request for a target low carbon behavior from a client; the low carbon point accounting request includes a user identification; the matching unit is used for calling the target interface information corresponding to the target low-carbon behavior under the condition that the target low-carbon behavior is determined to be matched with the first preset behavior set; the first preset behavior set comprises a plurality of first preset behaviors and corresponding interface information; each interface information is used for authorizing access to a database of the partner operator; the acquisition unit is used for acquiring first user behavior data corresponding to the user identifier from a target database corresponding to the target interface information; the verification unit is used for verifying whether the first user behavior data meets the low-carbon integral accounting condition corresponding to the target low-carbon behavior; an allocation execution unit, configured to allocate a target low-carbon integral corresponding to the target low-carbon behavior for the user identifier if the result of the verification is satisfied; and if the verification result is not satisfied, refusing to allocate the target low carbon integral aiming at the user identification, and feeding back accounting failure notification to the client.
The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the low-carbon integration accounting transaction method based on the cloud platform when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a cloud platform based low carbon point accounting transaction method as described in any of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements the cloud platform-based low-carbon point accounting transaction method as described in any one of the above.
According to the cloud platform-based low-carbon integral accounting transaction method, system, electronic equipment and non-transitory computer-readable storage medium, in the method, when a user requests to account for low-carbon integral aiming at low-carbon behaviors of a low-carbon cloud platform, a client only needs to automatically add user identification into access flow, a user is not required to arrange low-carbon evidence materials, the operation complexity of the user is reduced, and the low-carbon integral accounting experience of the user is improved. In addition, the low-carbon cloud platform directly calls the corresponding database to inquire corresponding user behavior data according to the user identification, and the behavior data are docked in a B to B mode, so that the uploading of fake low-carbon evidence to the low-carbon platform is completely avoided, and the authenticity of the behavior data and the reliability of a low-carbon integral distribution result are ensured. Further, the low-carbon cloud platform automatically verifies the user behavior data and the low-carbon integral accounting conditions, and automatically issues or refuses to issue corresponding low-carbon integral according to the verification result, so that the full-flow automatic low-carbon integral accounting transaction process is realized, the data auditing pressure of the low-carbon platform is reduced, the operation cost of the low-carbon platform is reduced, and the stable and orderly operation of the low-carbon platform is assisted.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a flow chart of an example of a cloud platform based low carbon point accounting transaction method according to an embodiment of the present invention;
FIG. 2 illustrates a flow chart of another example of a cloud platform based low carbon point accounting transaction method according to an embodiment of the present application;
FIG. 3 illustrates a flow chart of another example of a cloud platform based low carbon point accounting transaction method according to an embodiment of the present application;
FIG. 4 illustrates a flowchart of one example of operations for separately determining corresponding low carbon credits for each set low carbon behavior, in accordance with an embodiment of the present application;
FIG. 5 illustrates a block diagram of an example of a cloud platform based low carbon point accounting transaction system in accordance with an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
FIG. 1 illustrates a flow chart of an example of a cloud platform based low carbon point accounting transaction method according to an embodiment of the present application.
The implementation subject of the method of the embodiments of the present invention may be any controller or processor with computing or processing capabilities to achieve the goal of providing low carbon integrated accounting transaction services to client users. In some examples, it may be configured integrally in the server by software, hardware, or a combination of software and hardware, which should not be limited herein. The details of the technical scheme related to the invention will be described below by taking a low-carbon integration platform or a cloud platform server as an exemplary implementation main body.
As shown in fig. 1, in step S110, a low carbon integration accounting request for a target low carbon behavior is received from a client, the low carbon integration accounting request containing a user identification.
In combination with a service scenario, a user logs in a low-carbon integration platform by operating a client, and triggers an accounting control after completing low-carbon behavior so as to send a low-carbon integration accounting request to a service end, wherein the access flow aiming at the service platform should contain a user identifier.
In step S120, in the case that it is determined that the target low-carbon behavior matches the first preset behavior set, the target interface information corresponding to the target low-carbon behavior is invoked.
Here, the first preset behavior set includes a plurality of first preset behaviors and corresponding interface information, each of which is used to authorize access to a database of the partner operator. Accordingly, the first preset behavior refers to a low-carbon behavior that can be recorded by the partner operator.
In some embodiments, the cooperating operators may be various operating platforms for recording various low-carbon behaviors of citizens, such as shared single car operating platforms, bus operating platforms, subway operating platforms, and the like. Thus, the low carbon integration platform, by cooperating with the operator platform, accesses the database maintained by the operator platform with the operator platform opening interface information.
In step S130, first user behavior data corresponding to the user identifier is obtained from a target database corresponding to the target interface information.
Illustratively, user behavior data corresponding to low-carbon conditions is queried in the target database according to the user identification. For example, when the target low-carbon behavior requested to be calculated by the user is "bicycle riding behavior", whether the shared bicycle riding record of the user exists is queried in the shared bicycle operation database according to the user identification.
In step S140, it is verified whether the first user behavior data satisfies the low carbon integration accounting condition corresponding to the corresponding target low carbon behavior.
In combination with the above example, the low carbon integral accounting condition may be "the riding distance in seven days is accumulated to be more than 2 km", and correspondingly, by summing the riding distances corresponding to the shared bicycle riding records of the user in the last seven days, it is further identified whether the user accumulated riding distance is more than 2 km.
In step S151, if the result of the verification is satisfied, a target low carbon integral corresponding to the target low carbon behavior is assigned for the user identification.
In step S153, if the result of the verification is not satisfied, the allocation of the target low carbon integral for the user identification is refused, and the accounting failure notification is fed back to the client.
In combination with the above example, when the seven-day user accumulates the riding distance over 2 km, then the user is directly assigned a low carbon point corresponding to the low carbon point accounting condition "riding distance accumulated over 2 km within seven days".
It should be noted that the low carbon integration matched by the different low carbon behavior types should be differently set. In one example of an embodiment of the present application, the low carbon integration corresponding to each low carbon behavior may be self-setting. In another example of an embodiment of the present application, the low carbon integration corresponding to the different low carbon behaviors may also be automatically verified by an algorithm, as will be described in more detail below.
According to the embodiment of the application, when the user requests to calculate the low carbon integration aiming at the low carbon behavior to the low carbon cloud platform, the client only needs to automatically add the user identifier into the access flow, the user is not required to finish the low carbon evidence material, the operation complexity of the user is reduced, and the low carbon integration calculation experience of the user is improved. In addition, the low-carbon cloud platform directly calls the corresponding database to inquire corresponding user behavior data according to the user identification, and the behavior data are docked in a B to B mode, so that the uploading of fake low-carbon evidence to the low-carbon platform is completely avoided, and the authenticity of the behavior data and the reliability of a low-carbon integral distribution result are ensured. Further, the low-carbon cloud platform automatically verifies the user behavior data and the low-carbon integral accounting conditions, and automatically issues or refuses to issue corresponding low-carbon integral according to the verification result, so that the full-flow automatic low-carbon integral accounting transaction process is realized, the data auditing pressure of the low-carbon platform is reduced, the operation cost of the low-carbon platform is reduced, and the stable and orderly operation of the low-carbon platform is assisted.
Fig. 2 shows a flowchart of another example of a cloud platform-based low-carbon point accounting transaction method according to an embodiment of the present application.
As shown in fig. 2, in step S210, a low carbon integration accounting request for a target low carbon behavior is received from a client.
In step S220, under the condition that the target low-carbon behavior is determined to be matched with the second preset behavior set, a long connection is established with the client, and an evidence collection request is sent to the client, so that the client triggers the client to perform a screen video recording operation based on the evidence collection request, so as to generate a corresponding screen video.
Here, the second preset behavior may represent behavior data recorded by an operator having no cooperative relationship with the low-carbon cloud platform, such that the low-carbon cloud platform cannot be authorized to use the database API of the corresponding operation platform. At this time, low-carbon behavioral evidence needs to be uploaded by the user himself.
In addition, the client is further configured to detect a user operation during the recording of the screen video to generate at least one evidence screenshot corresponding to the targeted low-carbon behavior. In combination with a business scene, after triggering a screen recording function of the starting client, a user can open a corresponding APP (for example, an application platform which has no cooperative relation with a low-carbon cloud platform) with low-carbon behavior record and perform screen capturing, and at the moment, the whole process of screen capturing operation of the user is recorded by the screen recording video, so that a evidence for the evidence screenshot can be effectively formed.
In step S220, individual evidence screenshots responsive to the evidence collection request are continuously received from the client based on the long connection.
It should be noted that, a Long Connection is established between the server and the client, so that handshake Connection between the server and the client can be effectively prevented from being triggered again by uploading each evidence screenshot, and Connection establishment overhead and network delay are reduced.
In step S220, it is determined whether to assign a target low carbon credit for the user identification based on the respective evidence screenshot.
As described above, each uploaded evidence screenshot is generated during the recording, and the low carbon integration platform reduces the probability of receiving a counterfeit evidence screenshot by effectively verifying the authenticity of the source of the evidence screenshot through the recording video.
In step S230, a feedback notification is sent to the client and the long connection is broken. Here, feedback notification defines a message for notifying the user that the on-screen video and the evidence screenshot have been uploaded synchronously.
It should be noted that, because the file size of the video is large, it is not recommended to directly upload the file size to the cloud platform server through the long connection.
In an example of the embodiment of the present application, the cloud platform server may not set a transmission channel for the video recording, so that the video recording file is directly retained in the user's local area, and this way is unfavorable for the cloud operation and maintenance to complete the verification operation, but the screen capturing in the same screen recording process, and feedback notification, for example, notification messages are "the video recording video and the evidence screenshot are uploaded synchronously", which can already play a considerable role of psychological deterrence, and can reduce the proportion of counterfeit data to a certain extent.
In another example of the embodiment of the present application, the cloud platform server sends backup library interface information to the client, so that the client sends the video recording to the backup database corresponding to the backup library interface information. In other words, the flow of the client is guided to the backup server through the backup library interface information, in addition, the long connection between the cloud platform server and the client is turned off, the occupation of system resources is reduced, and the flow pressure on the low-carbon cloud platform can be effectively relieved.
FIG. 3 illustrates a flowchart of an example of a cloud platform based low carbon point accounting transaction method according to an embodiment of the present application.
As shown in fig. 3, in step S310, a low carbon integration accounting request for a target low carbon behavior is received from a client.
In step S320, based on the client identification information of the client, it is verified whether the client belongs to the platform-owned client.
In some embodiments, in addition to uploading the user identifier, the client may also simultaneously add corresponding client identifier information, such as IMEI, unified hardware code, etc., to the access request.
In step S330, when the client belongs to the platform self-owned client, the client sensing data corresponding to the preset time period is called according to the client identification information.
In combination with a business scene, the low-carbon platform operation direction citizens sell or distribute various low-carbon induction terminal equipment, and when a user carries the low-carbon induction terminal equipment to conduct low-carbon behaviors, the cloud platform service end can automatically sense corresponding user behavior data and analyze the data.
Here, the client sensing data is data collected based on at least one sensing module of the client, such as an acceleration module, a temperature sensing module, a positioning module, etc., so as to implement multi-dimensional collection of user behavior data.
In step S340, second user behavior data matching the client-side sensed data is determined.
In step S350, it is verified whether the second user behavior data satisfies the low carbon integration accounting condition.
According to the embodiment of the application, the low-carbon cloud platform directly detects and collects the behavior data of the user through the sensing module, and then the low-carbon integration condition of the behavior data of the user is checked, so that the user does not need to upload low-carbon behavior evidence, and the convenience and enthusiasm of the user for participating in low-carbon activities are improved.
FIG. 4 illustrates a flowchart of one example of operations to separately determine corresponding low carbon credits for each set low carbon behavior, according to an embodiment of the present application.
As shown in fig. 4, in step S410, a low carbon behavior set is acquired.
Here, the low carbon behavior set includes at least one set low carbon behavior. Specifically, the low-carbon behavior set can refer to the group standard "national green low-carbon behavior greenhouse gas emission reduction and chemical guide" and describes seven green low-carbon behavior groups of clothes, foods, lives, lines, uses, offices and digital finances, and a plurality of green low-carbon behaviors are arranged below each group, for example, "walking", "public transportation going" and "using new energy automobiles" are included in the "line", and a "scale" is provided as the carbon emission reduction amount for measuring, calculating and evaluating the national green behaviors.
In step S420, low-carbon behavior is set for each of the low-carbon behavior sets, and a low-carbon behavior reward specific gravity corresponding to the set low-carbon behavior is calculated.
In one example of an embodiment of the present application, the low-carbon platform operator manually assigns a corresponding low-carbon behavior rewards specific gravity for each set low-carbon behavior, respectively, e.g., a higher rewards specific gravity for heavily encouraged low-carbon behaviors. In another example of an embodiment of the present application, low carbon behavior for different settings may be determined by weight automation system analysis and calculation, more details of which will be developed below.
In step S430, the low carbon credits corresponding to each set low carbon behavior are determined according to the low carbon behavior rewarding specific gravity and the preset credit base value. More specifically, the low carbon points corresponding to the respective low carbon behaviors are "point basis value" and bonus specific gravity ", thereby achieving the goal of differentially assigning points for different low carbon behaviors.
In some embodiments, the bonus specific gravity for low carbon behavior is calculated by including:
Preset index weight for obtaining k low-carbon behavior evaluation indexes 。
The weight scores of the low-carbon behavior evaluation indexes are collected in a questionnaire mode or an expert investigation mode to determine the weight scores, and investigation results are input into a cloud platform server to accurately obtain the index weights of various low-carbon behaviors under different indexes.
And setting low-carbon behaviors for M low-carbon behavior sets, and constructing a low-carbon evaluation matrix I= (a ij)k×M,aij is a characteristic value of the j low-carbon behavior set in the I low-carbon behavior evaluation index for k low-carbon behavior set.
In some embodiments, the low carbon behavioral assessment indicator includes at least one of: low carbon emissions, public opinion attention and economic impact.
Based on the low-carbon evaluation matrix i= (a ij)k×M, calculating a relative dominance matrix o= (O ij)k×M) corresponding to the low-carbon evaluation matrix, wherein O ij is the relative dominance of the eigenvalue a ij:
；
For each set low-carbon behavior, calculating the comprehensive relative membership degree H j of the total weight vector H= (v 1,v2…,vm) of the relative membership degree relative to the set low-carbon behavior j according to the index expert weight rho corresponding to the set low-carbon behavior:
；
Wherein α is an optimization criterion parameter, α=1 corresponds to a least-squares power, and α=2 corresponds to a least-squares power; d is a distance parameter, d=1 and d=2 correspond to hamming distance and euclidean distance, respectively; t is alpha, d and takes the serial numbers of the group numbers corresponding to different values;
the calculation formula for calculating the low-carbon behavior rewarding proportion gamma i∈{γ1,γ2…,γM},γi corresponding to each set low-carbon behavior is as follows:
；
wherein T is the total number of groups of values of alpha and d, an 。
Therefore, by comprehensively analyzing the multi-index evaluation sample by using a multi-target fuzzy comprehensive evaluation algorithm based on relative optimum attributes, the weight relation between different targets (namely, low-carbon behaviors) can be effectively extracted, and the reliability of the finally determined index weight and the reliability of the low-carbon behavior rewarding proportion are further ensured.
As a further optimization of the embodiment of the present application, the preset index weight of the k low-carbon behavior evaluation index may also be determined by including the following ways:
A weight expert sample set s= { S 1,s2,…,sm } for k low-carbon behavior evaluation indexes u= { U 1,u2,…,uk } is obtained.
Here, the weight expert sample set contains a plurality of weight expert samples, each weight expert sample corresponding to a unique eco-technical expert, and the weight expert sample contains an index weight advice value b= { B 1,b2,…,bk } of the eco-technical expert for each low-carbon behavior evaluation index. In combination with a business scene, each expert participates in a technical seminar through organization experts, each expert distributes a questionnaire, and each environmental protection technical expert fills out the questionnaire to give index weights aiming at different evaluation indexes. Further, the results of each questionnaire are arranged into a sample, and then the results of each questionnaire are integrated to obtain a corresponding weight expert sample set.
Based on an enhanced analytic hierarchy process and a weight expert sample set, determining the index weight corresponding to each low-carbon behavior evaluation index specifically comprises:
based on the weight expert sample set, weight suggestion values of all environmental protection technical experts about low-carbon behavior evaluation index combinations are screened to construct corresponding paired comparison matrixes Q (n) = ; Wherein the low carbon behavior evaluation index combination includes low carbon behavior evaluation indexes u x and u y for comparison; n represents the total number of combinations of the corresponding low-carbon behavior evaluation index combinations determined based on the k low-carbon behavior evaluation indexes.
And counting the paired comparison matrixes corresponding to the low-carbon behavior evaluation index combinations to calculate corresponding total standard deviation sigma xy:
；
when sigma xy is more than or equal to 1, generating a sample set replacement notification aiming at the weight expert sample set;
When sigma xy is less than 1, for each low-carbon behavior evaluation index, calculating the average value of index weight suggested values for the low-carbon behavior evaluation index in each weight expert sample, and determining the index weight corresponding to the low-carbon behavior evaluation index according to the average value.
In the embodiment of the application, the weight expert sample set is constructed, the weight setting problem of different experts on the evaluation index is decomposed into factors of different layers by using a analytic hierarchy process, the factors are compared pairwise, and the opinion difference amplitude among the weight experts is reflected by the total standard deviation. Therefore, the subjective judgment of the expert and the objective calculation of the mathematical model are combined in a subjective and objective combination mode, the finally determined index weight can be ensured to be substantially consistent with the identification value of all the expert, and the reliability of the index weight determined for each low-carbon behavior evaluation index is ensured.
The cloud platform-based low-carbon point accounting transaction system provided by the invention is described below, and the cloud platform-based low-carbon point accounting transaction system described below and the cloud platform-based low-carbon point accounting transaction method described above can be correspondingly referred to each other.
Fig. 5 shows a block diagram of an example of a cloud platform based low carbon point accounting transaction system according to an embodiment of the present invention.
As shown in fig. 5, the cloud platform-based low-carbon point accounting transaction system 500 includes a receiving unit 510, a matching unit 520, an acquiring unit 530, a verifying unit 540, and an allocation executing unit 550.
The receiving unit 510 is configured to receive, from a client, a low-carbon integration accounting request for a target low-carbon behavior; the low carbon integration accounting request includes a user identification.
The matching unit 520 is configured to invoke target interface information corresponding to the target low-carbon behavior if it is determined that the target low-carbon behavior matches the first preset behavior set; the first preset behavior set comprises a plurality of first preset behaviors and corresponding interface information; each of the interface information is used to authorize access to a database of the partner operator.
The obtaining unit 530 is configured to obtain, from a target database corresponding to the target interface information, first user behavior data corresponding to the user identifier.
The verification unit 540 is configured to verify whether the first user behavior data meets a low carbon integration accounting condition corresponding to the target low carbon behavior.
The allocation execution unit 550 is configured to allocate a target low-carbon integral corresponding to the target low-carbon behavior for the user identifier if the result of the verification is satisfied; and if the verification result is not satisfied, refusing to allocate the target low carbon integral aiming at the user identification, and feeding back accounting failure notification to the client.
In some embodiments, embodiments of the present invention provide a non-transitory computer readable storage medium having stored therein one or more programs including execution instructions that are readable and executable by an electronic device (including, but not limited to, a computer, a server, or a network device, etc.) for performing the above-described cloud platform-based low-carbon integration accounting transaction method of the present invention.
In some embodiments, embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described cloud platform based low-carbon point accounting transaction method.
In some embodiments, the present invention further provides an electronic device, including: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a cloud platform based accounting transaction method for low-carbon integration.
Fig. 6 is a schematic hardware structure diagram of an electronic device for executing a low-carbon credit accounting transaction method based on a cloud platform according to another embodiment of the present invention, as shown in fig. 6, the device includes:
One or more processors 610, and a memory 620, one processor 610 being illustrated in fig. 6.
The apparatus for performing the accounting transaction method of low-carbon points based on the cloud platform may further include: an input device 630 and an output device 640.
The processor 610, memory 620, input devices 630, and output devices 640 may be connected by a bus or other means, for example in fig. 6.
The memory 620 is used as a non-volatile computer readable storage medium, and can be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as program instructions/modules corresponding to the cloud platform-based low-carbon integration accounting transaction method in the embodiment of the present invention. The processor 610 executes various functional applications of the server and data processing, that is, implements the low-carbon integration accounting transaction method based on the cloud platform according to the above-described method embodiment, by running nonvolatile software programs, instructions, and modules stored in the memory 620.
Memory 620 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the electronic device, etc. In addition, memory 620 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 620 optionally includes memory remotely located relative to processor 610, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 630 may receive input digital or character information and generate signals related to user settings and function control of the electronic device. The output device 640 may include a display device such as a display screen.
The one or more modules are stored in the memory 620 that, when executed by the one or more processors 610, perform the cloud platform based low-carbon point accounting transaction method of any of the method embodiments described above.
The product can execute the low-carbon point accounting transaction method based on the cloud platform, which is provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
The electronic device of the embodiments of the present invention exists in a variety of forms including, but not limited to:
(1) Mobile communication devices, which are characterized by mobile communication functionality and are aimed at providing voice, data communication. Such terminals include smart phones, multimedia phones, functional phones, low-end phones, and the like.
(2) Ultra mobile personal computer equipment, which belongs to the category of personal computers, has the functions of calculation and processing and generally has the characteristic of mobile internet surfing. Such terminals include PDA, MID, and UMPC devices, etc.
(3) Portable entertainment devices such devices can display and play multimedia content. The device comprises an audio player, a video player, a palm game machine, an electronic book, an intelligent toy and a portable vehicle navigation device.
(4) Other on-board electronic devices with data interaction functions, such as on-board devices mounted on vehicles.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the related art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (7)

1. A low-carbon point accounting transaction method based on a cloud platform is applied to a cloud platform server, and comprises the following steps:
Receiving a low carbon integrated accounting request from a client for a target low carbon behavior; the low carbon point accounting request includes a user identification;
Under the condition that the target low-carbon behavior is determined to be matched with a first preset behavior set, calling target interface information corresponding to the target low-carbon behavior; the first preset behavior set comprises a plurality of first preset behaviors and corresponding interface information; each interface information is used for authorizing access to a database of the partner operator;
Acquiring first user behavior data corresponding to the user identifier from a target database corresponding to the target interface information;
Verifying whether the first user behavior data meets a low carbon integral accounting condition corresponding to the target low carbon behavior;
if the verification result is satisfied, distributing a target low-carbon integral corresponding to the target low-carbon behavior for the user identification; and
If the verification result is not satisfied, rejecting to allocate the target low carbon integral for the user identifier, and feeding back a verification failure notification to the client;
before verifying whether the first user behavior data satisfies a low carbon integration accounting condition corresponding to the target low carbon behavior, the method further includes:
acquiring a low-carbon behavior set; the low carbon behavior set comprises at least one set low carbon behavior;
Aiming at each set low-carbon behavior in the low-carbon behavior set, calculating the low-carbon behavior rewarding proportion corresponding to the set low-carbon behavior;
determining low-carbon points corresponding to the set low-carbon behaviors respectively according to the low-carbon behavior rewarding proportion and a preset integral basic value;
wherein the low carbon behavioral rewards specific gravity is calculated by:
Preset index weight for obtaining k low-carbon behavior evaluation indexes ；
Setting low-carbon behaviors for M low-carbon behavior sets, and constructing a low-carbon evaluation matrix I= (a ij)k×M,aij is a characteristic value of the j-th low-carbon behavior set in the I-th low-carbon behavior evaluation index;
Based on the low-carbon evaluation matrix i= (a ij)k×M, calculating a relative dominance matrix o= (O ij)k×M) corresponding to the low-carbon evaluation matrix, wherein O ij is the relative dominance of the eigenvalue a ij:
；
For each set low-carbon behavior, calculating the comprehensive relative membership degree H j of the total weight vector H= (v 1,v2…,vm) of the relative membership degree relative to the set low-carbon behavior j according to the index expert weight rho corresponding to the set low-carbon behavior:
；
Wherein, α is an optimization criterion parameter, α=1 corresponds to a least square, and α=2 corresponds to a least square; d is a distance parameter, d=1 and d=2 correspond to hamming distance and euclidean distance, respectively; t is alpha, d and takes the serial numbers of the group numbers corresponding to different values;
the calculation formula for calculating the low-carbon behavior rewarding proportion gamma i∈{γ1,γ2…,γM },γi corresponding to each set low-carbon behavior is as follows:
；
Wherein T is the total number of groups of values of alpha and d, and ；
The obtaining the preset index weight of the k low-carbon behavior evaluation indexes includes:
Acquiring a weight expert sample set S= { S 1, s2,…, sm } aiming at the k low-carbon behavior evaluation indexes U= { U 1, u2,…, uk }; the weight expert sample set comprises a plurality of weight expert samples, each weight expert sample corresponds to a unique environmental protection technical expert, and the weight expert sample comprises an index weight proposal value B= { B 1, b2,…, bk } of the environmental protection technical expert for each low-carbon behavior evaluation index;
Based on an enhanced analytic hierarchy process and the weight expert sample set, determining the index weight corresponding to each low-carbon behavior evaluation index specifically comprises:
based on the weight expert sample set, weight suggestion values of all environmental protection technical experts about low-carbon behavior evaluation index combinations are screened to construct corresponding paired comparison matrixes Q (n) = ; Wherein the low carbon behavior evaluation index combination comprises low carbon behavior evaluation indexes u x and u y for comparison; n represents the total number of combinations corresponding to the low-carbon behavior evaluation index combinations determined based on the k low-carbon behavior evaluation indexes;
And counting the paired comparison matrixes corresponding to the low-carbon behavior evaluation index combinations to calculate corresponding total standard deviation sigma xy:
；
When sigma xy is more than or equal to 1, generating a sample set replacement notification for the weight expert sample set;
And when sigma xy is less than 1, counting the average value of index weight recommended values of the low-carbon behavior evaluation indexes in the weight expert samples according to the low-carbon behavior evaluation indexes, and determining the index weight corresponding to the low-carbon behavior evaluation indexes according to the average value.
2. The method of claim 1, wherein after receiving a low carbon integration accounting request from a client for target low carbon behavior, the method further comprises:
Under the condition that the target low-carbon behavior is matched with a second preset behavior set, establishing long connection with the client, and sending an evidence collection request to the client, so that the client triggers the client to perform screen video recording operation based on the evidence collection request to generate corresponding screen video; and the client is further configured to detect a user operation during on-screen video recording to generate at least one evidence screenshot corresponding to the target low-carbon behavior;
continuously receiving, from the client, respective ones of the evidence screenshots responsive to the evidence collection request based on the long connection;
based on each of the evidence screenshots, it is determined whether to assign the target low-carbon score for the user identification.
3. The method of claim 2, wherein, after the continuously receiving each of the evidence screenshots from the client in response to the evidence collection request based on the long connection, the method further comprises:
sending a feedback notice to the client and disconnecting the long connection; the feedback notification defines a message for notifying the user that the on-screen video and the evidence screenshot have been synchronously uploaded.
4. A method according to claim 2 or 3, wherein, after said continuously receiving each of said evidence screenshots from said client in response to said evidence collection request based on said long connection, said method further comprises:
And sending backup library interface information to the client so that the client sends the screen recording video to a backup database corresponding to the backup library interface information.
5. The method of claim 1, wherein after receiving a low carbon integration accounting request from a client for target low carbon behavior, the method further comprises:
Verifying whether the client belongs to a platform self-operating client or not based on the client identification information of the client;
When the client belongs to the platform self-operating client, the client sensing data corresponding to the preset time period is called according to the client identification information; the client sensing data is data acquired based on at least one sensing module of the client;
determining second user behavior data matched with the client-side sensing data;
Verifying whether the second user behavior data satisfies the low carbon integral accounting condition.
6. The method of claim 1, wherein the low-carbon behavioral assessment indicator comprises at least one of: low carbon emissions, public opinion attention and economic impact.
7. A cloud platform-based low-carbon point accounting transaction system, comprising:
a receiving unit configured to receive a low carbon integration accounting request for a target low carbon behavior from a client; the low carbon point accounting request includes a user identification;
The matching unit is used for calling the target interface information corresponding to the target low-carbon behavior under the condition that the target low-carbon behavior is determined to be matched with the first preset behavior set; the first preset behavior set comprises a plurality of first preset behaviors and corresponding interface information; each interface information is used for authorizing access to a database of the partner operator;
the acquisition unit is used for acquiring first user behavior data corresponding to the user identifier from a target database corresponding to the target interface information;
The verification unit is used for verifying whether the first user behavior data meets the low-carbon integral accounting condition corresponding to the target low-carbon behavior;
An allocation execution unit, configured to allocate a target low-carbon integral corresponding to the target low-carbon behavior for the user identifier if the result of the verification is satisfied; if the verification result is not satisfied, rejecting to allocate the target low carbon integral for the user identifier, and feeding back accounting failure notification to the client;
Before verifying whether the first user behavior data meets a low carbon integration accounting condition corresponding to the target low carbon behavior, the system is further configured to perform operations comprising:
acquiring a low-carbon behavior set; the low carbon behavior set comprises at least one set low carbon behavior;
Aiming at each set low-carbon behavior in the low-carbon behavior set, calculating the low-carbon behavior rewarding proportion corresponding to the set low-carbon behavior;
determining low-carbon points corresponding to the set low-carbon behaviors respectively according to the low-carbon behavior rewarding proportion and a preset integral basic value;
wherein the low carbon behavioral rewards specific gravity is calculated by:
Preset index weight for obtaining k low-carbon behavior evaluation indexes ；
Setting low-carbon behaviors for M low-carbon behavior sets, and constructing a low-carbon evaluation matrix I= (a ij)k×M,aij is a characteristic value of the j-th low-carbon behavior set in the I-th low-carbon behavior evaluation index;
Based on the low-carbon evaluation matrix i= (a ij)k×M, calculating a relative dominance matrix o= (O ij)k×M) corresponding to the low-carbon evaluation matrix, wherein O ij is the relative dominance of the eigenvalue a ij:
；
For each set low-carbon behavior, calculating the comprehensive relative membership degree H j of the total weight vector H= (v 1,v2…,vm) of the relative membership degree relative to the set low-carbon behavior j according to the index expert weight rho corresponding to the set low-carbon behavior:
；
Wherein, α is an optimization criterion parameter, α=1 corresponds to a least square, and α=2 corresponds to a least square; d is a distance parameter, d=1 and d=2 correspond to hamming distance and euclidean distance, respectively; t is alpha, d and takes the serial numbers of the group numbers corresponding to different values;
the calculation formula for calculating the low-carbon behavior rewarding proportion gamma i∈{γ1,γ2…,γM },γi corresponding to each set low-carbon behavior is as follows:
；
Wherein T is the total number of groups of values of alpha and d, and ；
The obtaining the preset index weight of the k low-carbon behavior evaluation indexes includes:
Acquiring a weight expert sample set S= { S 1, s2,…, sm } aiming at the k low-carbon behavior evaluation indexes U= { U 1, u2,…, uk }; the weight expert sample set comprises a plurality of weight expert samples, each weight expert sample corresponds to a unique environmental protection technical expert, and the weight expert sample comprises an index weight proposal value B= { B 1, b2,…, bk } of the environmental protection technical expert for each low-carbon behavior evaluation index;
Based on an enhanced analytic hierarchy process and the weight expert sample set, determining the index weight corresponding to each low-carbon behavior evaluation index specifically comprises:
based on the weight expert sample set, weight suggestion values of all environmental protection technical experts about low-carbon behavior evaluation index combinations are screened to construct corresponding paired comparison matrixes Q (n) = ; Wherein the low carbon behavior evaluation index combination comprises low carbon behavior evaluation indexes u x and u y for comparison; n represents the total number of combinations corresponding to the low-carbon behavior evaluation index combinations determined based on the k low-carbon behavior evaluation indexes;
And counting the paired comparison matrixes corresponding to the low-carbon behavior evaluation index combinations to calculate corresponding total standard deviation sigma xy:
；
When sigma xy is more than or equal to 1, generating a sample set replacement notification for the weight expert sample set;
And when sigma xy is less than 1, counting the average value of index weight recommended values of the low-carbon behavior evaluation indexes in the weight expert samples according to the low-carbon behavior evaluation indexes, and determining the index weight corresponding to the low-carbon behavior evaluation indexes according to the average value.
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