CN112434877A - Smart city data processing method and device based on cloud computing - Google Patents
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Abstract
The invention discloses a smart city data processing method and device based on cloud computing. According to the method, the ability of evaluating the data of the target city can be improved by acquiring the first city operation index data and the second city operation index data, the acquired data tag information is further converted into the characteristic data, and the key index information between the first city operation index data and the second city operation index data is determined based on the sequence value in the characteristic data, so that detailed investigation and data screening by workers can be avoided, planning and management of the city are performed through the acquired key index information, the timeliness of data processing is improved, and meanwhile, the labor cost can be obviously reduced.
Description
Technical Field
The disclosure relates to the technical field of smart cities and big data, in particular to a smart city data processing method and device based on cloud computing.
Background
Smart City (Smart City) is a City that uses various information technologies or innovative concepts to connect and integrate the system and service of the City to improve the efficiency of resource utilization, thereby optimizing City management and service and improving the quality of life of citizens. The smart city is a city informatization advanced form which fully applies a new generation of information technology to various industries in the city and is based on the innovation of the next generation of knowledge society, realizes the deep integration of informatization, industrialization and urbanization, is beneficial to relieving the large urban diseases, improves the urbanization quality, realizes the fine and dynamic management, improves the urban management effect and improves the quality of life of citizens.
With the wide application of technologies such as cloud computing and big data, the modern city management cannot be satisfied by the traditional city data processing, for example: the traditional planning management of the city mainly comprises that workers conduct detailed investigation and data screening, so that the timeliness of data processing is low, and the labor cost is obviously increased.
Disclosure of Invention
In order to solve the technical problems in the related art, the disclosure provides a smart city data processing method and device based on cloud computing.
The invention provides a smart city data processing method based on cloud computing, which comprises the following steps:
acquiring first city operation index data and second city operation index data of a target city in an operation process; the first city operation index data and the second city operation index data are city operation index data corresponding to an operation image shot by the target city in the operation process;
acquiring data label information of the first city operation index data and the second city operation index data; wherein the data tag information characterizes tags between corresponding data lists between the first city operation index data and the second city operation index data;
converting the data tag information into characteristic data; wherein the feature data comprises a plurality of data lists;
and determining key index information between the first city operation index data and the second city operation index data according to the sequence value of the data list in the feature data.
In an alternative embodiment, the obtaining data tag information of the first city operation index data and the second city operation index data specifically includes:
determining first dimension vector information corresponding to the first city operation index data and second dimension vector information corresponding to the second city operation index data;
fusing the first dimension vector information and the second dimension vector information to obtain the data label information; the data label information is expressed in a form of fusion dimension, and the fusion dimension comprises fusion degrees corresponding to the label information among the data lists.
In an alternative embodiment, the converting the data tag information into feature data includes: acquiring the fusion degree in the fusion dimension corresponding to the data tag information; converting the fusion degree into a sequence value to obtain the characteristic data; wherein the characteristic data is expressed in the form of a sequence value matrix.
In an alternative embodiment, the determining key indicator information between the first city operation indicator data and the second city operation indicator data according to the sequence value of the data list in the feature data includes: determining a maximum sequence value of the data list from the feature data; determining key indicator information between the first city operation indicator data to the second city operation indicator data in response to the maximum sequence value being less than a sequence value requirement.
In an alternative embodiment, the key indicator information includes traffic information, the method further comprising: and realizing traffic route guidance according to the traffic information.
The invention also provides a smart city data processing device based on cloud computing, which comprises:
the operation index data acquisition module is used for acquiring first city operation index data and second city operation index data of a target city in the operation process; the first city operation index data and the second city operation index data are city operation index data corresponding to an operation image shot by the target city in the operation process;
the data tag information acquisition module is used for acquiring data tag information of the first city operation index data and the second city operation index data; wherein the data tag information characterizes tags between corresponding data lists between the first city operation index data and the second city operation index data;
the data label information conversion module is used for converting the data label information into characteristic data; wherein the feature data comprises a plurality of data lists;
and the key index information determining module is used for determining key index information between the first city operation index data and the second city operation index data according to the sequence value of the data list in the feature data.
In an alternative embodiment, the data tag information collection module is specifically configured to:
determining first dimension vector information corresponding to the first city operation index data and second dimension vector information corresponding to the second city operation index data;
fusing the first dimension vector information and the second dimension vector information to obtain the data label information; the data label information is expressed in a form of fusion dimension, and the fusion dimension comprises fusion degrees corresponding to the label information among the data lists.
In an alternative embodiment, the data tag information conversion module is specifically configured to: acquiring the fusion degree in the fusion dimension corresponding to the data tag information; converting the fusion degree into a sequence value to obtain the characteristic data; wherein the characteristic data is expressed in the form of a sequence value matrix.
In an alternative embodiment, the key indicator information determining module is specifically configured to: determining a maximum sequence value of the data list from the feature data; determining key indicator information between the first city operation indicator data to the second city operation indicator data in response to the maximum sequence value being less than a sequence value requirement.
In an alternative embodiment, the apparatus further comprises a traffic guidance module, specifically configured to: and realizing traffic route guidance according to the traffic information.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The utility model provides a smart city data processing method and device based on cloud computing, through obtaining first city operation index data and second city operation index data, can improve the ability of evaluating the data in target city, further convert the data label information that obtains into the characteristic data, and then confirm the key index information between first city operation index data and the second city operation index data based on the sequence value in the characteristic data, can avoid surveying and screening data in detail through the staff like this, but plan the management through the key index information that obtains to the city, and then improve the timeliness of data processing, can show simultaneously and reduce the human cost.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart of a smart city data processing method based on cloud computing according to an embodiment of the present invention.
Fig. 2 is a block diagram of a smart city data processing apparatus based on cloud computing according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of a cloud computing server according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In order to improve the traditional planning management of cities, workers mainly conduct detailed investigation and data screening, so that the timeliness of data processing is low, and the labor cost is remarkably increased.
To achieve the above object, please refer to fig. 1, which is a flowchart illustrating a smart city data processing method based on cloud computing according to an embodiment of the present invention, wherein the following steps S110 to S140 are specifically performed when the method is implemented.
Step S110, acquiring first city operation index data and second city operation index data of a target city in the operation process.
In this embodiment, the first city operation index data and the second city operation index data are city operation index data corresponding to an operation image captured in an operation process of the target city.
Step S120, obtaining data label information of the first city operation index data and the second city operation index data.
In this embodiment, the data tag information represents a tag between corresponding data lists between the first city operation index data and the second city operation index data.
Step S130, converting the data label information into characteristic data; wherein the feature data comprises a plurality of data lists.
Step S140, determining key index information between the first city operation index data and the second city operation index data according to the sequence value of the data list in the feature data.
The following advantageous effects can be achieved when the method described in the above steps S110 to S140 is performed:
the method has the advantages that the capability of evaluating the data of the target city can be improved by acquiring the first city operation index data and the second city operation index data, the acquired data tag information is further converted into the characteristic data, and then the key index information between the first city operation index data and the second city operation index data is determined based on the sequence value in the characteristic data, so that detailed investigation and data screening by workers can be avoided, the city is planned and managed through the acquired key index information, the timeliness of data processing is improved, and meanwhile, the labor cost can be obviously reduced.
In an alternative embodiment, the obtaining data tag information of the first city operation index data and the second city operation index data specifically includes:
determining first dimension vector information corresponding to the first city operation index data and second dimension vector information corresponding to the second city operation index data;
fusing the first dimension vector information and the second dimension vector information to obtain the data label information; the data label information is expressed in a form of fusion dimension, and the fusion dimension comprises fusion degrees corresponding to the label information among the data lists.
In an alternative embodiment, the converting the data tag information into feature data includes: acquiring the fusion degree in the fusion dimension corresponding to the data tag information; converting the fusion degree into a sequence value to obtain the characteristic data; wherein the characteristic data is expressed in the form of a sequence value matrix.
In an alternative embodiment, the determining key indicator information between the first city operation indicator data and the second city operation indicator data according to the sequence value of the data list in the feature data includes: determining a maximum sequence value of the data list from the feature data; determining key indicator information between the first city operation indicator data to the second city operation indicator data in response to the maximum sequence value being less than a sequence value requirement.
In an alternative embodiment, the key indicator information includes traffic information, the method further comprising: and realizing traffic route guidance according to the traffic information.
Preferably, the method for implementing traffic route guidance according to the traffic information further includes the following step S300: acquiring target traffic information in a to-be-detected block; extracting road traffic information from the target traffic information; determining a first matching degree between the road traffic flow information and preset traffic flow planning information; the traffic flow planning information is used for representing road environment information which does not change along with time change; when a first matching degree between target road traffic flow information in the road traffic flow information and preset traffic flow planning information meets a congestion scheduling condition, planning a traffic flow route guidance strategy corresponding to the target road traffic flow information in the target traffic information; and sending the traffic flow route guiding strategy to a target user terminal.
In this way, firstly, the road traffic flow information is extracted from the acquired target traffic information, then when a first matching degree between the target road traffic flow information in the road traffic flow information and preset traffic flow planning information meets a congestion scheduling condition, a traffic flow route guidance strategy corresponding to the target road traffic flow information is planned in the target traffic information, and the traffic flow route guidance strategy is sent to a target user terminal.
In this way, by extracting the road traffic information from the acquired target traffic information, the traffic information of the road and the driving rate of the vehicle can be quickly analyzed from the road traffic information. When the first matching degree between the target road traffic flow information and the preset traffic flow planning information is further judged to meet the congestion scheduling condition, a corresponding traffic flow route guiding strategy is planned for the target road traffic flow information, so that a user does not need to listen to a broadcast to know the traffic problem, the traffic flow route guiding strategy is actively pushed to a mobile phone, the accurate guiding of the traffic flow can be timely realized, the problem of traffic congestion can be effectively avoided, and convenience can be brought to the user for going out.
Further, the air conditioner is provided with a fan,
the extracting of the road traffic information from the target traffic information includes:
preprocessing the target traffic information;
performing first feature extraction on the preprocessed target traffic information to obtain current road traffic information;
and performing secondary feature extraction on the current road traffic flow information to obtain road traffic flow information corresponding to the target traffic information.
Therefore, the current road traffic flow information is obtained by performing the first characteristic extraction on the processed target traffic information, and the second characteristic extraction is further performed on the current road traffic flow information, so that the road traffic flow information can be accurately obtained through multiple times of characteristic extraction.
The current road traffic information comprises initial road traffic information, first current road traffic information and second current road traffic information; the first feature extraction is performed on the preprocessed target traffic information to obtain current road traffic information, and the method comprises the following steps:
carrying out information identification processing on the preprocessed target traffic information through a plain text identification unit in a preset information extraction thread to obtain initial road traffic information;
performing first feature extraction on the initial road traffic flow information through a first feature extraction unit in the preset information extraction thread to obtain first current road traffic flow information;
and performing first feature extraction on the first current road traffic flow information through a second feature extraction unit in the preset information extraction thread to obtain second current road traffic flow information.
The second feature extraction is performed on the current road traffic flow information to obtain the road traffic flow information corresponding to the target traffic information, and the method comprises the following steps:
performing second feature extraction on the second current road traffic flow information through a first feature extraction unit in the preset information extraction thread to obtain second road traffic flow information;
performing secondary feature extraction on the second road traffic flow information through a second feature extraction unit in the preset information extraction thread to obtain actual road traffic flow information;
and fusing the initial road traffic flow information, the second road traffic flow information, the actual road traffic flow information and the traffic flow corresponding to each road in the preprocessed target traffic information to obtain road traffic flow information.
Further, on the basis of step 300, the method further includes:
performing road state analysis on the first current road traffic flow information through a first thread state list in the preset information extraction thread to obtain first passing vehicle state information;
performing secondary feature extraction on the second road traffic flow information to obtain actual road traffic flow information;
performing second feature extraction on fusion processing information consisting of the first passing vehicle state information and the second road traffic flow information to obtain target road traffic flow information;
performing road state analysis on the initial road traffic flow information through a second thread state list of the preset information extraction thread to obtain target initial road traffic flow information;
the fusing the initial road traffic information, the second road traffic information, the actual road traffic information and the preprocessed traffic flow corresponding to each road in the target traffic information comprises: and fusing the target initial road traffic flow information, the actual road traffic flow information, the target road traffic flow information and the traffic flow corresponding to each road in the preprocessed target traffic information.
Further, the step of configuring the preset information extraction thread includes:
acquiring road information to be planned; mapping the road information to the preset information extraction thread, and extracting the configuration road traffic flow information of each lane in the road information;
calculating the matching degree of the configured road traffic flow information and the road information to be planned to obtain a second matching degree; the road information to be planned belongs to thread information in the preset information extraction thread;
calculating a deviation value according to the determined second matching degree; and verifying the information of each thread in the preset information extraction threads based on the calculated deviation values, and stopping configuration until the verified deviation value corresponding to the preset information extraction threads is smaller than the preset deviation value.
In this way, the acquired road information to be planned is firstly mapped into the preset information extraction thread, so that the configured road traffic flow information of each lane in the road information can be efficiently extracted. And further calculating a deviation value according to the determined second matching degree, and checking each thread information in the preset information extraction thread based on the deviation value, so that the accuracy of each thread information in the preset information extraction thread can be determined.
Further, on the basis of step 300, the method further includes: and when the preset information extraction thread is stopped being configured, taking the road information to be planned corresponding to the configured preset information extraction thread as the traffic flow planning information.
Further, the air conditioner is provided with a fan,
the determining a first matching degree between the road traffic flow information and preset traffic flow planning information includes: inputting the road traffic flow information into a two-dimensional matrix to obtain a road traffic flow information distribution track; calculating a first matching degree between the road traffic information distribution track and preset traffic planning information;
the step of inputting the road traffic flow information into a two-dimensional matrix to obtain a road traffic flow information distribution track comprises the following steps: and performing information identification processing on the road traffic flow information by adopting thread interface information of a preset information extraction thread to obtain a road traffic flow information distribution track of a two-dimensional matrix.
The calculating a first matching degree between the road traffic information distribution track and preset traffic planning information includes:
calculating a difference threshold value between the road traffic flow information and preset traffic flow planning information;
calculating the sudden change incidence rate of the flow sudden change mode of each lane in the target traffic information in the corresponding traffic flow interval by using the difference threshold;
summing the calculated mutation occurrence rates to obtain mutation occurrence rates and values; performing stage matching proportion analysis on the mutation occurrence rate and the mutation value to obtain a matching proportion;
and determining the matching ratio as a first matching degree between the road traffic information and the corresponding traffic planning information.
Further, planning a traffic route guidance strategy corresponding to the target road traffic information in the target traffic information includes:
determining first traffic jam information corresponding to first target traffic information and second traffic jam information corresponding to second target traffic information, wherein the first traffic jam information and the second traffic jam information respectively comprise a plurality of jam duration records with different weights; extracting initial traffic flow information of the first target traffic information recorded in any blocking time of the first traffic blocking information, and determining a blocking time record with the minimum weight in the second traffic blocking information as a target blocking time record; loading the initial traffic flow information into the target blocking time length record according to a preset loading mode and a preset loading protocol, obtaining an initial loading list in the target blocking time length record, and determining a first traffic list relation between the first target traffic information and the second target traffic information based on the initial traffic flow information and the initial loading list;
acquiring target road traffic information in the target blocking time record based on the initial loading list, loading the target road traffic information into the blocking time record of the initial traffic flow information according to a second traffic list relation corresponding to the first traffic list relation, acquiring key road traffic information corresponding to the target road traffic information in the blocking time record of the initial traffic flow information, and determining key traffic section information of the key road traffic information as target traffic flow information; acquiring actual traffic state information loaded into the target blocking time length record by the initial traffic flow information;
according to the association degrees between the key road traffic flow information and the traffic flow information to be detected corresponding to a plurality of lists to be associated on the actual traffic state information, sequentially acquiring road map information corresponding to the target traffic flow information from the second traffic jam information until the list of the congestion duration record of the acquired road map information is consistent with the list of the target traffic flow information in the first traffic jam information, and stopping acquiring the road map information in the next congestion duration record;
receiving a road map information analysis instruction; the road map information analysis instruction comprises a road name search mode of road map information to be analyzed, wherein the road name search mode refers to a road name search mode stored by a server; acquiring corresponding static road information according to the received road map information analysis instruction; the static road information is reference road information of each road map information to be analyzed; determining a plurality of sub-road information to be analyzed of the road map information to be analyzed according to the road name searching mode; each piece of sub-road information to be analyzed comprises a plurality of road interference factors;
acquiring road comprehensive information corresponding to each piece of sub-road information to be analyzed, and coding the plurality of pieces of sub-road information to be analyzed according to the road comprehensive information; identifying specific characteristic information of each road interference factor in the first sub-road information to be analyzed according to the road comprehensive information of the first sub-road information to be analyzed for the first sub-road information to be analyzed in the encoded plurality of sub-road information to be analyzed;
identifying road interference factors of corresponding characteristic information from the static road information based on the specific characteristic information, and determining each road interference factor according to the road interference factor corresponding to the corresponding characteristic information and a preset road determination function so as to determine first target road information; the preset road determining function is used for defining a determining method of each road interference factor in the road map information to be analyzed;
de-noising the first target road information, deleting the noise information in the de-noised first target road information, and loading the noise information into a road planning text which accords with the road name searching mode and is stored in a server; the denoising processing comprises redundant information eliminating processing; and according to the determining, denoising and adding modes of the first target road information, sequentially performing determining, denoising and adding operations on other sub-road information to be analyzed in the encoded sub-road information to be analyzed until a corresponding traffic flow route guidance strategy is determined.
In this way, the extracted initial traffic flow information is loaded into the determined target blocking time duration record, so that an initial loading list can be recorded in the target blocking time duration record in real time, a first traffic list relation between first target traffic information and second target traffic information is further determined based on the initial traffic flow information and the initial loading list, so that key road traffic flow information can be obtained quickly, further, key traffic flow section information of the key road traffic flow information is used as the target traffic flow information, actual traffic state information obtained after the target traffic flow information is determined and loaded into the target blocking time duration record by the initial traffic flow information is obtained, and therefore the accuracy of traffic state judgment can be improved. And then the road map information corresponding to the target traffic flow information is acquired from the second traffic jam information, so that the road information of each road can be found out intuitively and clearly according to the road map information. And analyzing first target road information after the road map information is determined, and then sequentially analyzing a plurality of pieces of target road information. And then a traffic route guidance strategy is analyzed according to the information of the plurality of target roads, so that the traffic road can be accurately regulated and controlled through the traffic route guidance strategy, and the traffic road is effectively prevented from being jammed.
On the basis, please refer to fig. 2, the present invention further provides a smart city data processing apparatus 200 based on cloud computing, the apparatus comprising:
an operation index data obtaining module 210, configured to obtain first city operation index data and second city operation index data of a target city in an operation process; the first city operation index data and the second city operation index data are city operation index data corresponding to an operation image shot by the target city in the operation process;
a data tag information acquisition module 220, configured to acquire data tag information of the first city operation index data and the second city operation index data; wherein the data tag information characterizes tags between corresponding data lists between the first city operation index data and the second city operation index data;
a data tag information conversion module 230, configured to convert the data tag information into feature data; wherein the feature data comprises a plurality of data lists;
a key indicator information determining module 240, configured to determine key indicator information between the first city operation indicator data and the second city operation indicator data according to the sequence value of the data list in the feature data.
In an alternative embodiment, the data tag information collecting module 220 is specifically configured to:
determining first dimension vector information corresponding to the first city operation index data and second dimension vector information corresponding to the second city operation index data;
fusing the first dimension vector information and the second dimension vector information to obtain the data label information; the data label information is expressed in a form of fusion dimension, and the fusion dimension comprises fusion degrees corresponding to the label information among the data lists.
In an alternative embodiment, the data tag information conversion module 230 is specifically configured to: acquiring the fusion degree in the fusion dimension corresponding to the data tag information; converting the fusion degree into a sequence value to obtain the characteristic data; wherein the characteristic data is expressed in the form of a sequence value matrix.
In an alternative embodiment, the key indicator information determining module 240 is specifically configured to: determining a maximum sequence value of the data list from the feature data; determining key indicator information between the first city operation indicator data to the second city operation indicator data in response to the maximum sequence value being less than a sequence value requirement.
In an alternative embodiment, the apparatus further comprises a traffic directing module 250, specifically configured to: and realizing traffic route guidance according to the traffic information.
On the basis, please refer to fig. 3 in combination, which provides a cloud computing server 300, including a processor 310, a memory 320 connected to the processor 310, and a bus 330; wherein, the processor 310 and the memory 320 communicate with each other through the bus 330; the processor 310 is used to call the program instructions in the memory 320 to execute the above-mentioned method.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A smart city data processing method based on cloud computing is characterized by comprising the following steps:
acquiring first city operation index data and second city operation index data of a target city in an operation process; the first city operation index data and the second city operation index data are city operation index data corresponding to an operation image shot by the target city in the operation process;
acquiring data label information of the first city operation index data and the second city operation index data; wherein the data tag information characterizes tags between corresponding data lists between the first city operation index data and the second city operation index data;
converting the data tag information into characteristic data; wherein the feature data comprises a plurality of data lists;
and determining key index information between the first city operation index data and the second city operation index data according to the sequence value of the data list in the feature data.
2. The method according to claim 1, wherein the obtaining of the data tag information of the first city operation index data and the second city operation index data specifically includes:
determining first dimension vector information corresponding to the first city operation index data and second dimension vector information corresponding to the second city operation index data;
fusing the first dimension vector information and the second dimension vector information to obtain the data label information; the data label information is expressed in a form of fusion dimension, and the fusion dimension comprises fusion degrees corresponding to the label information among the data lists.
3. The method of claim 2, wherein converting the data tag information into feature data comprises: acquiring the fusion degree in the fusion dimension corresponding to the data tag information; converting the fusion degree into a sequence value to obtain the characteristic data; wherein the characteristic data is expressed in the form of a sequence value matrix.
4. The method according to any one of claims 1 to 3, wherein the determining key indicator information between the first city operation indicator data and the second city operation indicator data according to the sequence value of the data list in the feature data comprises: determining a maximum sequence value of the data list from the feature data; determining key indicator information between the first city operation indicator data to the second city operation indicator data in response to the maximum sequence value being less than a sequence value requirement.
5. The method of claim 1, wherein the key indicator information comprises traffic information, the method further comprising: and realizing traffic route guidance according to the traffic information.
6. A smart city data processing apparatus based on cloud computing, the apparatus comprising:
the operation index data acquisition module is used for acquiring first city operation index data and second city operation index data of a target city in the operation process; the first city operation index data and the second city operation index data are city operation index data corresponding to an operation image shot by the target city in the operation process;
the data tag information acquisition module is used for acquiring data tag information of the first city operation index data and the second city operation index data; wherein the data tag information characterizes tags between corresponding data lists between the first city operation index data and the second city operation index data;
the data label information conversion module is used for converting the data label information into characteristic data; wherein the feature data comprises a plurality of data lists;
and the key index information determining module is used for determining key index information between the first city operation index data and the second city operation index data according to the sequence value of the data list in the feature data.
7. The apparatus of claim 6, wherein the data tag information collection module is specifically configured to:
determining first dimension vector information corresponding to the first city operation index data and second dimension vector information corresponding to the second city operation index data;
fusing the first dimension vector information and the second dimension vector information to obtain the data label information; the data label information is expressed in a form of fusion dimension, and the fusion dimension comprises fusion degrees corresponding to the label information among the data lists.
8. The apparatus of claim 7, wherein the data tag information conversion module is specifically configured to: acquiring the fusion degree in the fusion dimension corresponding to the data tag information; converting the fusion degree into a sequence value to obtain the characteristic data; wherein the characteristic data is expressed in the form of a sequence value matrix.
9. The apparatus according to any one of claims 6 to 8, wherein the key indicator information determining module is specifically configured to: determining a maximum sequence value of the data list from the feature data; determining key indicator information between the first city operation indicator data to the second city operation indicator data in response to the maximum sequence value being less than a sequence value requirement.
10. The device according to claim 6, characterized in that it further comprises a traffic guidance module, in particular for: and realizing traffic route guidance according to the traffic information.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117033889A (en) * | 2023-08-02 | 2023-11-10 | 瀚能科技有限公司 | Smart park production data statistics method and related device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117033889A (en) * | 2023-08-02 | 2023-11-10 | 瀚能科技有限公司 | Smart park production data statistics method and related device |
| CN117033889B (en) * | 2023-08-02 | 2024-04-05 | 瀚能科技有限公司 | Smart park production data statistics method and related device |
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