CN115687875A - Smart campus management method and system and SaaS cloud platform - Google Patents
Smart campus management method and system and SaaS cloud platform Download PDFInfo
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Abstract
The application relates to the technical field of smart campuses and data management, and relates to a smart campus management method, a smart campus management system and a SaaS cloud platform. In the application, the first response priority vector is determined according to the regression analysis confidence of the campus interactive data theme output by the data analysis thread and the campus interactive data recording event, the second response priority vector is determined according to the regression analysis confidence of the campus interactive data theme output by the data analysis thread and the campus interactive data evaluation theme, and the first response priority vector and the second response priority vector are more in line with the characteristics of the data analysis thread, so that the data analysis thread is debugged by using the target quantization evaluation vector determined by the first response priority vector and the second response priority vector together, the accuracy and the reliability of the campus interactive data tag obtained by the debugged data analysis thread through regression analysis are better, and the campus interactive data are managed and processed more reliably and accurately.
Description
Technical Field
The application relates to the technical field of smart campuses and data management, in particular to a smart campus management method, a smart campus management system and a SaaS cloud platform.
Background
Data management is the process of efficiently collecting, storing, processing, and applying data using computer hardware and software techniques. The purpose of this is to fully and effectively play the role of data. The key to achieving efficient management of data is data organization.
With the development of computer technology, data management goes through three development stages of manual management, file systems and database systems. The built data structure in the database system describes the internal relation among data more fully, is convenient for data modification, updating and expansion, ensures the independence, reliability, safety and integrity of the data, reduces the data redundancy, and improves the data sharing degree and the data management efficiency.
When the data management technology is specifically applied to the smart campus, the problem that interaction data of the smart campus are inaccurate or interfered may exist. Therefore, it is difficult to manage and process the campus interactive data more reliably and accurately.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a smart campus management method and system and a SaaS cloud platform.
In a first aspect, a smart campus management method is provided, which is applied to a SaaS cloud platform, and the method at least includes: acquiring a template campus interactive data management scheme, and outputting regression analysis confidence of a campus interactive data theme corresponding to the template campus interactive data management scheme through a data analysis thread; determining a campus interaction data evaluation theme corresponding to the template campus interaction data management scheme by combining the regression analysis confidence of the campus interaction data theme; if the campus interaction data evaluation theme is different from the campus interaction data recording event of the template campus interaction data management scheme, determining a first response priority vector corresponding to the campus interaction data recording event and a second response priority vector corresponding to the campus interaction data evaluation theme one by one according to the regression analysis confidence of the campus interaction data theme; the first response priority vector is a priority level determined based on a first original quantitative assessment vector and the template campus interaction data management scheme, the first original quantitative assessment vector is determined by combining a regression analysis confidence of the campus interaction data topic and the campus interaction data recording event; the second response priority vector is a priority level determined based on a second original quantitative assessment vector and the template campus interaction data management scheme, the second original quantitative assessment vector is determined by combining a regression analysis confidence of the campus interaction data topic and the campus interaction data assessment topic; and determining a target quantization evaluation vector of the template campus interaction data management scheme by combining the first response priority vector and the second response priority vector, debugging the data analysis thread by combining the target quantization evaluation vector to obtain a target data analysis thread, and managing and processing the campus interaction data based on the target data analysis thread.
In an independently implemented embodiment, the template campus interaction data management scheme is a campus interaction data queue; the determining a first response priority vector corresponding to the campus interaction data recording event and a second response priority vector corresponding to the campus interaction data evaluation topic one by one according to the regression analysis confidence of the campus interaction data topic includes: determining a first original quantitative evaluation vector of the campus interaction data queue by combining the regression analysis confidence of the campus interaction data topic and the campus interaction data recording event; taking a difference vector between the first original quantitative evaluation vector and the campus interaction data queue as the first response priority vector corresponding to the campus interaction data recording event; determining a second original quantitative evaluation vector of the campus interaction data queue by combining the regression analysis confidence of the campus interaction data topic and the campus interaction data evaluation topic; and taking a difference vector between the second original quantitative evaluation vector and the campus interaction data queue as the second response priority vector corresponding to the campus interaction data evaluation subject.
In an independently implemented embodiment, the determining a target quantization evaluation vector of the template campus interaction data management scheme in combination with the first response priority vector and the second response priority vector includes: obtaining a quantitative evaluation model coefficient; determining an association between the first response priority vector and the second response priority vector; and combining the quantitative evaluation model coefficient, the correlation condition and the first original quantitative evaluation vector to build the target quantitative evaluation vector of the template campus interactive data management scheme.
In an independently implemented embodiment, the building the target quantization evaluation vector of the template campus interaction data management scheme by combining the quantization evaluation model coefficients, the association condition, and the first original quantization evaluation vector includes: splicing the quantitative evaluation model coefficient and the correlation condition, and taking an un-spliced successful vector of a splicing result as an abnormal template quantitative evaluation vector of the template campus interactive data management scheme; and splicing the abnormal template quantitative evaluation vector and the first original quantitative evaluation vector to obtain the target quantitative evaluation vector.
In an independently implemented embodiment, the template campus interaction data management scheme includes an abnormal template campus interaction data management scheme and a standard template campus interaction data management scheme; the debugging the data analysis thread by combining the target quantization evaluation vector to obtain a target data analysis thread comprises the following steps: generating a standard template quantitative evaluation vector corresponding to the standard template campus interactive data management scheme by combining the regression analysis confidence of the campus interactive data topic corresponding to the standard template campus interactive data management scheme and a campus interactive data recording event; and debugging the data analysis thread by combining the target quantitative evaluation vector of the abnormal template campus interactive data management scheme and the standard template quantitative evaluation vector of the standard template campus interactive data management scheme to obtain the target data analysis thread.
In an embodiment of independent implementation, the debugging the data analysis thread by combining the target quantitative evaluation vector of the abnormal template campus interaction data management scheme and the standard template quantitative evaluation vector of the standard template campus interaction data management scheme to obtain a target data analysis thread includes: acquiring the overall number of standard template campus interaction data management schemes and abnormal template campus interaction data management schemes covered in the template campus interaction data management schemes; splicing a target model quantitative vector of the abnormal template campus interactive data management scheme with a standard template campus interactive data management scheme quantitative evaluation vector of the standard template campus interactive data management scheme, and determining a depolarization quantitative evaluation vector according to a spliced result and the global number; if the depolarization quantitative evaluation vector does not accord with a thread specified condition, debugging the thread coefficient of the data analysis thread by combining the depolarization quantitative evaluation vector, and taking the debugged data analysis thread as the target data analysis thread when the debugged data analysis thread accords with the thread specified condition; and if the depolarization quantitative evaluation vector meets the thread specified condition, taking the data analysis thread as the target data analysis thread.
In a separately implemented embodiment, the method further comprises: acquiring target campus interaction data, and inputting the target campus interaction data into the target data analysis thread; outputting regression analysis confidence degrees of not less than two target campus interactive data topics corresponding to the target campus interactive data by using the target data analysis thread; and determining the regression analysis confidence coefficient of the maximum target campus interactive data theme according to the regression analysis confidence coefficients of the at least two target campus interactive data themes, and taking the campus interactive data label corresponding to the regression analysis confidence coefficient of the maximum target campus interactive data theme as the campus interactive data label corresponding to the target campus interactive data.
In a separately implemented embodiment, the method further comprises: determining an interaction strategy of the target campus interaction data by combining the campus interaction data tag corresponding to the target campus interaction data; if the target campus interaction data is an interaction strategy with errors, the target campus interaction data is used as campus interaction data with errors, and an interaction range with errors is determined in the campus interaction data with errors; carrying out error recording on the interaction range with the error in the campus interaction data with the error, and outputting the campus interaction data with the error record and the error; and if the target campus interaction data is a standard interaction strategy, taking the target campus interaction data as standard campus interaction data, and outputting the standard campus interaction data.
In a separately implemented embodiment, the determining an interaction policy for the target campus interaction data in conjunction with the campus interaction data tag includes: if the campus interactive data tag corresponding to the target campus interactive data is a campus interactive data tag with an error, taking an interaction strategy of the target campus interactive data as an interaction strategy with the error; and if the campus interaction data tag corresponding to the target campus interaction data is a standard campus interaction data tag, taking the interaction strategy of the target campus interaction data as a standard interaction strategy.
In an embodiment of an independent implementation, the determining, in the error campus interaction data, an error interaction range includes: acquiring a campus interactive data label corresponding to the campus interactive data with errors, and acquiring an interactive range filtering thread with errors of a target associated with the campus interactive data label corresponding to the campus interactive data with errors in an interactive range filtering thread set with errors; and loading the campus interaction data with errors to an interaction range filtering thread with errors in the target, and determining an interaction range with errors in the campus interaction data with errors by using the interaction range filtering thread with errors in the target.
In a second aspect, a smart campus management system is provided, comprising: the system comprises a SaaS cloud platform and a campus data acquisition end, wherein the SaaS cloud platform is in communication connection with the campus data acquisition end;
wherein, the SaaS cloud platform is used for: acquiring a template campus interactive data management scheme, and outputting regression analysis confidence of a campus interactive data theme corresponding to the template campus interactive data management scheme through a data analysis thread; determining a campus interaction data evaluation topic corresponding to the template campus interaction data management scheme by combining the regression analysis confidence of the campus interaction data topic; if the campus interaction data evaluation theme is different from the campus interaction data recording event of the template campus interaction data management scheme, determining a first response priority vector corresponding to the campus interaction data recording event and a second response priority vector corresponding to the campus interaction data evaluation theme one by one according to the regression analysis confidence of the campus interaction data theme; the first response priority vector is a priority level determined based on a first original quantitative assessment vector and the template campus interaction data management scheme, the first original quantitative assessment vector is determined by combining a regression analysis confidence of the campus interaction data topic and the campus interaction data recording event; the second response priority vector is a priority level determined based on a second original quantitative assessment vector and the template campus interaction data management scheme, the second original quantitative assessment vector is determined by combining a regression analysis confidence of the campus interaction data topic and the campus interaction data assessment topic; and determining a target quantization evaluation vector of the template campus interaction data management scheme by combining the first response priority vector and the second response priority vector, debugging the data analysis thread by combining the target quantization evaluation vector to obtain a target data analysis thread, and managing and processing the campus interaction data based on the target data analysis thread.
In a third aspect, a SaaS cloud platform is provided, which includes: a memory for storing a computer program; a processor coupled to the memory for executing the computer program stored by the memory to implement the method described above.
According to the intelligent campus management method, the intelligent campus management system and the SaaS cloud platform, a target quantization evaluation vector of a template campus interaction data management scheme is determined through a first response priority vector of a campus interaction data recording event of the template campus interaction data management scheme and a second response priority vector of a campus interaction data evaluation theme of the template campus interaction data management scheme, when the target quantization evaluation vector is used, the target quantization evaluation vector can be minimized, so that different places between the first response priority vector and the second response priority vector are maximized, and a data analysis thread can be continuously updated through a debugging method for continuously maximizing the difference between the first response priority vector and the second response priority vector, so that the data analysis thread can pay attention to the places with standards (such as the first response priority vector corresponding to the campus interaction data recording event), and the campus interaction data evaluation theme analyzed by the data analysis thread can be continuously close to the campus interaction data recording event; and because the first response priority vector is determined according to the regression analysis confidence of the campus interactive data theme output by the data analysis thread and the campus interactive data recording event, and the second response priority vector is determined according to the regression analysis confidence of the campus interactive data theme output by the data analysis thread and the campus interactive data evaluation theme, the first response priority vector and the second response priority vector are more in line with the characteristics of the data analysis thread, the data analysis thread is debugged by using a target quantization evaluation vector determined by the first response priority vector and the second response priority vector together, so that the accuracy and reliability of the campus interactive data label obtained by regression analysis of the debugged data analysis thread are better, and the campus interactive data is managed and processed more reliably and accurately.
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To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a smart campus management method according to an embodiment of the present disclosure.
Fig. 2 is a block diagram of a smart campus management device according to an embodiment of the present disclosure.
Fig. 3 is a schematic diagram of a hardware structure of a SaaS cloud platform according to an embodiment of the present disclosure.
Detailed Description
In order to better understand the technical solutions of the present application, the following detailed descriptions are provided with accompanying drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and in a case of no conflict, the technical features in the embodiments and examples of the present application may be combined with each other.
Referring to fig. 1, a method for smart campus management is shown, which may include the following steps S101-S103.
Step S101, obtaining a template campus interaction data management scheme, and outputting a regression analysis confidence coefficient of a campus interaction data theme corresponding to the template campus interaction data management scheme through a data analysis thread.
The template campus interaction data management scheme can be used for configuring a data analysis thread, and the data analysis thread can be updated through configuration of the template campus interaction data management scheme.
Step S102, determining a campus interaction data evaluation topic corresponding to the template campus interaction data management scheme by combining the regression analysis confidence of the campus interaction data topic; if the campus interaction data evaluation theme is different from the campus interaction data recording event of the template campus interaction data management scheme, determining a first response priority vector corresponding to the campus interaction data recording event and a second response priority vector corresponding to the campus interaction data evaluation theme one by one according to the regression analysis confidence of the campus interaction data theme; the first answer priority vector is a priority level determined based on a first original quantitative assessment vector determined in conjunction with a regression analysis confidence of the campus interactive data topic and the campus interactive data recording event with the template campus interactive data management scheme; the second response priority vector is a priority determined based on a second original quantitative assessment vector determined in conjunction with a regression analysis confidence for the campus interaction data topic and the template campus interaction data management schema.
In the present disclosure, the maximum regression analysis confidence of the campus interactive data topic may be determined from the regression analysis confidence of the campus interactive data topics output by the data analysis thread, and the campus interactive data tag corresponding to the regression analysis confidence of the maximum campus interactive data topic is used as the campus interactive data evaluation topic of the data analysis thread regression analysis.
In the present disclosure, the template campus interactive data management scheme is loaded into a data analysis thread (e.g., CNN thread), and the CNN thread will understand the template campus interactive data management scheme as a campus interactive data queue covering many values. Here, the campus interactive data recording event is a real-time campus interactive data tag to which the template campus interactive data management scheme belongs. According to the regression analysis confidence coefficient of the campus interactive data theme output by the data analysis thread and the campus interactive data recording event, a first original quantitative evaluation vector of the campus interactive data queue can be determined; and determining a second original quantitative evaluation vector of the campus interactive data queue according to the regression analysis confidence of the campus interactive data topic output by the data analysis thread and the campus interactive data evaluation topic.
According to a second original quantitative evaluation vector of the campus interactive data queue (a cross entropy quantitative evaluation vector determined by a campus interactive data evaluation subject), a difference vector between the second original quantitative evaluation vector and an input template campus interactive data management scheme (a campus interactive data queue) can be determined, and the difference vector can be used as a second response priority vector corresponding to the campus interactive data evaluation subject.
Step S103, determining a target quantization evaluation vector of the template campus interaction data management scheme by combining the first response priority vector and the second response priority vector, debugging the data analysis thread by combining the target quantization evaluation vector to obtain a target data analysis thread, and performing campus interaction data management processing based on the target data analysis thread.
In the present disclosure, according to the first response priority vector and the second response priority vector of the template campus interactive data management scheme, a target quantization evaluation vector of the template campus interactive data management scheme may be determined, and a specific method for determining the target quantization evaluation vector may determine an abnormal template quantization evaluation vector of the template campus interactive data management scheme first, and then determine the abnormal template quantization evaluation vector together with the first original quantization evaluation vector corresponding to the template campus interactive data management scheme according to the abnormal template quantization evaluation vector.
Further, after the target quantization evaluation vector is obtained, the data analysis thread (e.g., CNN thread) may be debugged according to the target quantization evaluation vector to obtain the target data analysis thread. It can be seen that the target quantization evaluation vector is set up for template campus interaction data management schemes with different campus interaction data evaluation topics and different campus interaction data recording events, in a general case, when a CNN thread is configured, the template campus interaction data management scheme loaded to the CNN thread includes at least two types of campus interaction data, in the template campus interaction data management schemes, if the campus interaction data evaluation topic of the template campus interaction data management scheme X is different from the campus interaction data recording event, it can be shown that the CNN thread has a regression analysis error for the template campus interaction data management scheme X, and the input template campus interaction data management scheme X can be used as an abnormal template campus interaction data management scheme, that is, the CNN thread is difficult to perform a regression analysis standard for the template campus interaction data management scheme X; in these template campus interactive data management schemes, if the regression analysis of the template campus interactive data management scheme Y by the CNN thread is standard, the input template campus interactive data management scheme Y may be used as a standard template campus interactive data management scheme or a simple template campus interactive data management scheme, that is, the CNN thread may easily perform the regression analysis on the campus interactive data label of the template campus interactive data management scheme Y. That is to say, in the template campus interaction data management scheme loaded to the CNN thread, a standard template campus interaction data management scheme and an abnormal template campus interaction data management scheme are included, and for debugging the CNN thread, the CNN thread can be debugged together according to the standard template campus interaction data management scheme and the abnormal template campus interaction data management scheme. The specific method for debugging the CNN thread (data analysis thread) according to the standard template campus interaction data management scheme and the abnormal template campus interaction data management scheme may be to generate a standard template quantitative evaluation vector corresponding to the standard template campus interaction data management scheme according to a regression analysis confidence of a campus interaction data topic corresponding to the standard template campus interaction data management scheme and a campus interaction data recording event.
After the configuration is completed, that is, the data analysis thread is debugged, the target data analysis thread may be put into a campus interactive data recognition scene, that is, the campus interactive data management processing may be performed based on the target data analysis thread. The step of performing the campus interaction data management processing by the target data analysis thread may be to obtain target campus interaction data and load the target campus interaction data to the target data analysis thread; through the target data analysis thread, regression analysis confidence degrees of at least two target campus interaction data topics corresponding to the target campus interaction data can be output; in the regression analysis confidence degrees of the at least two target campus interactive data topics, the regression analysis confidence degree of the maximum target campus interactive data topic can be determined, and the campus interactive data tag corresponding to the regression analysis confidence degree of the maximum target campus interactive data topic is used as the campus interactive data tag corresponding to the target campus interactive data.
The method comprises the steps that the first response priority vector of a campus interaction data recording event of a template campus interaction data management scheme and the second response priority vector of a campus interaction data evaluation subject of the template campus interaction data management scheme are determined by utilizing the conductibility of a feature extraction thread, so that a target quantization evaluation vector of the template campus interaction data management scheme is determined together, when the target quantization evaluation vector is utilized, the target quantization evaluation vector can be minimized, so that different places exist between the first response priority vector and the second response priority vector are maximized, and the data analysis thread can be continuously updated by continuously maximizing a debugging method of the difference between the first response priority vector and the second response priority vector, so that the data analysis thread can pay attention to the standard places (such as the first response priority vector corresponding to the campus interaction data recording event), and the campus interaction data evaluation subject regressed by the data analysis thread can be continuously close to the interaction data recording event; and because the first response priority vector is determined according to the regression analysis confidence coefficient of the campus interaction data theme output by the data analysis thread and the campus interaction data recording event, and the second response priority vector is determined according to the regression analysis confidence coefficient of the campus interaction data theme output by the data analysis thread and the campus interaction data evaluation theme, the first response priority vector and the second response priority vector are more in line with the characteristics of the data analysis thread, so that the data analysis thread is debugged by using the target quantization evaluation vector determined by the first response priority vector and the second response priority vector together, the accuracy and the reliability of the campus interaction data label obtained by the debugged data analysis thread through regression analysis can be better, and the campus interaction data can be managed and processed more reliably and accurately.
Based on the above, the following steps can be further included.
Step S201, target campus interactive data are obtained and input into the target data analysis thread.
Step S202, outputting regression analysis confidence degrees of not less than two target campus interactive data subjects corresponding to the target campus interactive data through the target data analysis thread.
Step S203, determining the regression analysis confidence of the largest target campus interactive data topic according to the regression analysis confidences of the at least two target campus interactive data topics, and using the campus interactive data tag corresponding to the regression analysis confidence of the largest target campus interactive data topic as the campus interactive data tag corresponding to the target campus interactive data.
In the present disclosure, the target recognition thread may extract a target campus interactive data feature of the target campus interactive data, and output, according to the target campus interactive data feature, a regression analysis confidence of at least two target campus interactive data topics corresponding to the target campus interactive data, where the regression analysis confidence of each target campus interactive data topic corresponds to one campus interactive data tag, and then the campus interactive data tag corresponding to the maximum regression analysis confidence of the target campus interactive data topic may be used as the campus interactive data tag for the target campus interactive data regression analysis by the target data analysis thread.
And step S204, determining an interaction strategy of the target campus interaction data according to the campus interaction data tag corresponding to the target campus interaction data.
In the scheme, if the campus interactive data tag corresponding to the target campus interactive data is the wrong campus interactive data tag, the interaction strategy of the target campus interactive data can be used as the wrong interaction strategy; if the campus interaction data tag corresponding to the target campus interaction data is a standard campus interaction data tag, the interaction policy of the target campus interaction data can be used as a standard interaction policy.
Step S205, if the target campus interaction data is an erroneous interaction policy, taking the target campus interaction data as erroneous campus interaction data, and determining an erroneous interaction range in the erroneous campus interaction data.
In this disclosure, if the interaction policy of the target campus interaction data is an interaction policy with an error, the target campus interaction data may be used as the campus interaction data with the error, and an interaction range with the error is determined in the campus interaction data with the error. In the method, a campus interactive data label corresponding to the campus interactive data with errors is obtained, and in the filtering thread set with the errors, an interactive range filtering thread with errors in a target associated with the campus interactive data label corresponding to the campus interactive data with errors is obtained; and loading the campus interaction data with errors into the interaction range filtering thread with errors in the target, and determining the interaction range with errors in the campus interaction data with errors through the interaction range filtering thread with errors in the target. The interaction range filtering thread with an error in the target associated with the campus interaction data tag corresponding to the campus interaction data with an error may be a thread in which the tag with the error in the interaction range filtering is matched with the campus interaction data tag corresponding to the campus interaction data with an error.
Step S206, performing error logging on the error interaction range in the error campus interaction data, and outputting the error campus interaction data with the error logging.
In the present disclosure, the error recording may be performed by framing an error-existing interaction range in the target campus interaction data (error-existing campus interaction data); and the interaction range with errors in the campus interaction data with errors can be extracted and output together with the campus interaction data with errors.
It should be understood that, after the target data analysis thread determines the campus interactive data tag to which the target campus interactive data belongs, if the campus interactive data tag is an erroneous campus interactive data tag, the target campus interactive data having the erroneous campus interactive data tag may be loaded into an erroneous target interaction range filtering thread corresponding to the campus interactive data tag, and through the erroneous target interaction range filtering thread, the erroneous interaction range in the target campus interactive data may be identified, and the erroneous interaction range in the target campus interactive data may be recorded erroneously, and then the erroneous campus interactive data having the erroneous record is output. That is to say, the data analysis thread may determine the campus interaction data tag to which the target campus interaction data belongs, and the interaction range filtering thread in which the target has an error may determine the interaction range in which the target campus interaction data has an error. The data analysis thread is originally performing campus interaction data tag classification, while the interaction scope filtering thread with errors is further and more specifically detail recognition.
Step S207, if the target campus interactive data is a standard interactive policy, taking the target campus interactive data as standard campus interactive data, and outputting the standard campus interactive data.
In the present disclosure, if the target campus interactive data is the standard interactive policy, the target campus interactive data and the campus interactive data tag to which the target campus interactive data belongs may be directly output.
The method comprises the steps that by means of the conductibility of a feature extraction thread, a first response priority vector of a campus interaction data recording event of an abnormal template campus interaction data management scheme (template campus interaction data management scheme) and a second response priority vector of a campus interaction data evaluation theme of the abnormal template campus interaction data management scheme are calculated; then, a target quantization evaluation vector of the abnormal template campus interactive data management scheme is determined together according to the first response priority vector and the second response priority vector, when the feature extraction thread (data analysis thread) is configured by using the target quantization evaluation vector, the target quantization evaluation vector can be minimized, so that different places between the first response priority vector and the second response priority vector are maximized, the feature extraction thread can be continuously updated by a debugging method for continuously maximizing the difference between the first response priority vector and the second response priority vector, the feature extraction thread can continuously pay attention to a standard place (such as the first response priority vector corresponding to a campus interactive data recording event), and a campus interactive data evaluation theme returned by the feature extraction thread can continuously approach to the campus interactive data recording event; and because the first response priority vector is determined according to the regression analysis confidence coefficient of the campus interactive data theme output by the feature extraction thread and the campus interactive data recording event, the second response priority vector is determined according to the regression analysis confidence coefficient of the campus interactive data theme output by the graph feature extraction thread and the campus interactive data evaluation theme, the first response priority vector and the second response priority vector are determined based on the characteristics of the feature extraction thread, and the target quantization evaluation vector constructed by the first response priority vector and the second response priority vector is more consistent with the characteristics of the feature extraction thread. Therefore, the feature extraction thread is debugged by using the target quantitative evaluation vector, so that the feature extraction thread is more excellent, and the campus interactive data label of the campus interactive data can be more accurately regressed and analyzed by the target data analysis thread obtained through debugging. When the target data analysis thread is applied, campus interaction data labels of the target campus interaction data are classified through the target data analysis thread, the interaction range with errors can be further utilized to filter the thread, the interaction range with errors is determined in the target campus interaction data, and therefore the interaction range with errors can be rapidly and effectively identified, and the identification range of the interaction range with errors can be reduced.
On the basis, please refer to fig. 2 in combination, which provides a smart campus management apparatus 200 applied to a smart campus management system, the apparatus includes:
the data analysis module 210 is configured to obtain a template campus interaction data management scheme, and output a regression analysis confidence of a campus interaction data topic corresponding to the template campus interaction data management scheme through a data analysis thread;
the theme evaluation module 220 is configured to determine, according to the regression analysis confidence of the campus interaction data theme, a campus interaction data evaluation theme corresponding to the template campus interaction data management scheme; if the campus interactive data evaluation topic is different from the campus interactive data recording events of the template campus interactive data management scheme, determining a first response priority vector corresponding to the campus interactive data recording events and a second response priority vector corresponding to the campus interactive data evaluation topic one by combining the regression analysis confidence of the campus interactive data topics; the first answer priority vector is a priority level determined based on a first original quantitative assessment vector determined in conjunction with a regression analysis confidence of the campus interactive data topic and the campus interactive data recording event with the template campus interactive data management scheme; the second response priority vector is a priority level determined based on a second original quantitative assessment vector determined in conjunction with a regression analysis confidence of the campus interactive data topic and the template campus interactive data management scheme;
the data management module 230 is configured to determine a target quantization evaluation vector of the template campus interaction data management scheme in combination with the first response priority vector and the second response priority vector, debug the data analysis thread in combination with the target quantization evaluation vector to obtain a target data analysis thread, and perform campus interaction data management processing based on the target data analysis thread.
On the basis of the above, referring to fig. 3, a smart campus management system 300 is shown, which includes a processor 310 and a memory 320, which are in communication with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.
On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.
In summary, based on the above-mentioned solution, a target quantization evaluation vector of the template campus interaction data management scheme is determined by determining a first response priority vector of a campus interaction data recording event of the template campus interaction data management scheme and a second response priority vector of a campus interaction data evaluation topic of the template campus interaction data management scheme, and when the target quantization evaluation vector is utilized, the target quantization evaluation vector can be minimized, so that a place where the first response priority vector and the second response priority vector are different is maximized, and by a debugging method that continuously maximizes a difference between the first response priority vector and the second response priority vector, a data analysis thread can be continuously updated, so that the data analysis thread can pay attention to a standard place (e.g., the first response priority vector corresponding to the campus interaction data recording event), and thus, the campus interaction data evaluation topic that the data analysis thread regresses to analyze can be continuously close to the campus interaction data recording event; and because the first response priority vector is determined according to the regression analysis confidence of the campus interactive data theme output by the data analysis thread and the campus interactive data recording event, and the second response priority vector is determined according to the regression analysis confidence of the campus interactive data theme output by the data analysis thread and the campus interactive data evaluation theme, the first response priority vector and the second response priority vector are more in line with the characteristics of the data analysis thread, the data analysis thread is debugged by using a target quantization evaluation vector determined by the first response priority vector and the second response priority vector together, so that the accuracy and reliability of the campus interactive data label obtained by regression analysis of the debugged data analysis thread are better, and the campus interactive data is managed and processed more reliably and accurately.
It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features are required than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit-preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, and the like, cited in this application is hereby incorporated by reference in its entirety. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those explicitly described and illustrated herein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A smart campus management method is characterized by being applied to a SaaS cloud platform and at least comprising the following steps:
acquiring a template campus interactive data management scheme, and outputting regression analysis confidence of a campus interactive data theme corresponding to the template campus interactive data management scheme through a data analysis thread;
determining a campus interaction data evaluation theme corresponding to the template campus interaction data management scheme by combining the regression analysis confidence of the campus interaction data theme; if the campus interactive data evaluation topic is different from the campus interactive data recording events of the template campus interactive data management scheme, determining a first response priority vector corresponding to the campus interactive data recording events and a second response priority vector corresponding to the campus interactive data evaluation topic one by combining the regression analysis confidence of the campus interactive data topics; the first response priority vector is a priority level determined based on a first original quantitative assessment vector and the template campus interaction data management scheme, the first original quantitative assessment vector is determined by combining a regression analysis confidence of the campus interaction data topic and the campus interaction data recording event; the second response priority vector is a priority level determined based on a second original quantitative assessment vector determined in conjunction with a regression analysis confidence of the campus interactive data topic and the template campus interactive data management scheme;
and determining a target quantization evaluation vector of the template campus interaction data management scheme by combining the first response priority vector and the second response priority vector, debugging the data analysis thread by combining the target quantization evaluation vector to obtain a target data analysis thread, and managing and processing the campus interaction data based on the target data analysis thread.
2. The method of claim 1, wherein said template campus interaction data management scheme is a campus interaction data queue; the determining a first response priority vector corresponding to the campus interaction data recording event and a second response priority vector corresponding to the campus interaction data evaluation topic one by one according to the regression analysis confidence of the campus interaction data topic includes:
determining a first original quantitative evaluation vector of the campus interaction data queue by combining a regression analysis confidence of the campus interaction data topic and the campus interaction data recording event;
taking a difference vector between the first original quantitative evaluation vector and the campus interaction data queue as the first response priority vector corresponding to the campus interaction data recording event;
determining a second original quantitative evaluation vector of the campus interactive data queue by combining the regression analysis confidence of the campus interactive data topic and the campus interactive data evaluation topic;
and taking the difference vector between the second original quantitative evaluation vector and the campus interaction data queue as the second response priority vector corresponding to the campus interaction data evaluation subject.
3. The method of claim 1, wherein said determining a target quantization assessment vector for said template campus interaction data management scheme in combination with said first response priority vector and said second response priority vector comprises:
obtaining a quantitative evaluation model coefficient;
determining an association between the first response priority vector and the second response priority vector;
and combining the quantitative evaluation model coefficient, the correlation condition and the first original quantitative evaluation vector to build the target quantitative evaluation vector of the template campus interactive data management scheme.
4. The method of claim 3, wherein said building the target quantitative evaluation vector of the template campus interaction data management scenario in combination with the quantitative evaluation model coefficients, the correlation case, and the first raw quantitative evaluation vector comprises:
splicing the quantitative evaluation model coefficient and the correlation condition, and taking an un-spliced successful vector of a splicing result as an abnormal template quantitative evaluation vector of the template campus interactive data management scheme;
and splicing the abnormal template quantitative evaluation vector and the first original quantitative evaluation vector to obtain the target quantitative evaluation vector.
5. The method of claim 1, wherein the template campus interaction data management scheme comprises an exception template campus interaction data management scheme and a standard template campus interaction data management scheme; the debugging the data analysis thread by combining the target quantization evaluation vector to obtain a target data analysis thread comprises the following steps:
generating a standard template quantitative evaluation vector corresponding to the standard template campus interactive data management scheme by combining the regression analysis confidence of the campus interactive data topic corresponding to the standard template campus interactive data management scheme and a campus interactive data recording event;
and debugging the data analysis thread by combining the target quantitative evaluation vector of the abnormal template campus interactive data management scheme and the standard template quantitative evaluation vector of the standard template campus interactive data management scheme to obtain the target data analysis thread.
6. The method of claim 5, wherein the debugging the data analysis thread in combination with the target quantitative evaluation vector of the abnormal template campus interaction data management scheme and the standard template quantitative evaluation vector of the standard template campus interaction data management scheme to obtain a target data analysis thread comprises:
acquiring the overall number of standard template campus interaction data management schemes and abnormal template campus interaction data management schemes covered in the template campus interaction data management schemes;
splicing a target model quantitative vector of the abnormal template campus interactive data management scheme with a standard template campus interactive data management scheme quantitative evaluation vector of the standard template campus interactive data management scheme, and determining a depolarization quantitative evaluation vector according to a spliced result and the global number;
if the depolarization quantitative evaluation vector does not meet the thread specified condition, debugging the thread coefficient of the data analysis thread by combining the depolarization quantitative evaluation vector, and taking the debugged data analysis thread as the target data analysis thread when the debugged data analysis thread meets the thread specified condition;
and if the depolarization quantitative evaluation vector meets the thread specified condition, taking the data analysis thread as the target data analysis thread.
7. The method of claim 1, further comprising:
acquiring target campus interaction data, and inputting the target campus interaction data into the target data analysis thread;
outputting regression analysis confidence degrees of not less than two target campus interaction data topics corresponding to the target campus interaction data by using the target data analysis thread;
and determining the regression analysis confidence coefficient of the maximum target campus interactive data topic according to the regression analysis confidence coefficients of the at least two target campus interactive data topics, and taking the campus interactive data label corresponding to the regression analysis confidence coefficient of the maximum target campus interactive data topic as the campus interactive data label corresponding to the target campus interactive data.
8. The method of claim 7, further comprising:
determining an interaction strategy of the target campus interaction data by combining the campus interaction data tag corresponding to the target campus interaction data;
if the target campus interaction data is an interaction strategy with errors, the target campus interaction data is used as campus interaction data with errors, and an interaction range with errors is determined in the campus interaction data with errors;
carrying out error recording on the interaction range with the error in the campus interaction data with the error, and outputting the campus interaction data with the error recorded and the error;
if the target campus interactive data is a standard interactive strategy, the target campus interactive data is used as standard campus interactive data, and the standard campus interactive data are output;
wherein, the determining the interaction strategy of the target campus interaction data by combining the campus interaction data tag comprises:
if the campus interactive data tag corresponding to the target campus interactive data is a campus interactive data tag with an error, taking an interaction strategy of the target campus interactive data as an interaction strategy with the error;
if the campus interaction data tag corresponding to the target campus interaction data is a standard campus interaction data tag, taking an interaction strategy of the target campus interaction data as a standard interaction strategy;
wherein, the determining the interaction range with errors in the campus interaction data with errors comprises:
acquiring a campus interaction data tag corresponding to the campus interaction data with errors, and acquiring an interaction range filtering thread with errors of a target associated with the campus interaction data tag corresponding to the campus interaction data with errors in an interaction range filtering thread set with errors;
and loading the campus interaction data with errors to the interaction range filtering thread with errors in the target, and determining the interaction range with errors in the campus interaction data with errors by using the interaction range filtering thread with errors in the target.
9. A smart campus management system, comprising: the system comprises a SaaS cloud platform and a campus data acquisition terminal, wherein the SaaS cloud platform is in communication connection with the campus data acquisition terminal;
wherein, the SaaS cloud platform is used for: acquiring a template campus interactive data management scheme, and outputting regression analysis confidence of a campus interactive data theme corresponding to the template campus interactive data management scheme through a data analysis thread; determining a campus interaction data evaluation theme corresponding to the template campus interaction data management scheme by combining the regression analysis confidence of the campus interaction data theme; if the campus interactive data evaluation topic is different from the campus interactive data recording events of the template campus interactive data management scheme, determining a first response priority vector corresponding to the campus interactive data recording events and a second response priority vector corresponding to the campus interactive data evaluation topic one by combining the regression analysis confidence of the campus interactive data topics; the first response priority vector is a priority level determined based on a first original quantitative assessment vector and the template campus interaction data management scheme, the first original quantitative assessment vector is determined by combining a regression analysis confidence of the campus interaction data topic and the campus interaction data recording event; the second response priority vector is a priority level determined based on a second original quantitative assessment vector determined in conjunction with a regression analysis confidence of the campus interactive data topic and the template campus interactive data management scheme; and determining a target quantization evaluation vector of the template campus interaction data management scheme by combining the first response priority vector and the second response priority vector, debugging the data analysis thread by combining the target quantization evaluation vector to obtain a target data analysis thread, and managing and processing the campus interaction data based on the target data analysis thread.
10. A SaaS cloud platform, comprising:
a memory for storing a computer program;
a processor coupled to the memory for executing the computer program stored by the memory to implement the method of any of claims 1-8.
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| CN202211238775.3A CN115687875A (en) | 2022-10-11 | 2022-10-11 | Smart campus management method and system and SaaS cloud platform |
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