CN111400293A - Multi-dimensional parameter statistical method and device for collection data - Google Patents
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Abstract
The utility model provides a multidimensional parameter statistical method of collection data, which is used for acquiring transaction orders and data of merchants; processing the transaction order and data of the merchant through a flink distributed computing system to obtain the payment data to be counted by the merchant; inputting the collection time data into a first parameter extraction model to obtain hidden layer output characteristics of the first parameter extraction model; inputting the data of the cash receiving place into a second parameter extraction model to obtain hidden layer output characteristics of the second parameter extraction model; inputting the data of the main collection body into a third parameter extraction model to obtain hidden layer output characteristics of the third parameter extraction model; and (5) performing feature extraction on the to-be-counted collection data and finishing counting output display. The method can safely, conveniently and accurately complete the statistical operation of the collection data, and provides usability for subsequent analysis and decision-making based on the collection data. The disclosure also provides a multi-dimensional parameter statistical device of the collection data.
Description
Technical Field
The disclosure relates to the technical field of intelligent hardware and mobile payment, in particular to a multi-dimensional parameter statistical method and device for collection data.
Background
In the prior art, different merchants have the function of counting and inquiring the weekly, monthly and quarterly collection conditions of a single merchant or related chain merchants. How to realize the requirement more accurately through various parameters is a problem to be solved urgently at the present stage.
Disclosure of Invention
In order to solve technical problems in the prior art, embodiments of the present disclosure provide a method and an apparatus for multidimensional parameter statistics of collection data, which can safely, conveniently, and accurately complete collection data statistics, and provide usability for subsequent analysis and decision based on collection data.
In a first aspect, an embodiment of the present disclosure provides a multi-dimensional parameter statistical method for collection data, which is applied to an electronic device, where the electronic device is provided with a double-sided asynchronous liquid crystal display assembly, the double-sided asynchronous liquid crystal display assembly includes a display screen, a 3D structured light recognition module, and a positioning module, and the method includes: acquiring a transaction order and data of a merchant; processing the transaction order and data of the merchant through a flink distributed computing system to obtain the payment data to be counted by the merchant, wherein the payment data comprises: collection time data, collection place data and collection main body data; inputting the collection time data into a first parameter extraction model to obtain hidden layer output characteristics of the first parameter extraction model, wherein the first parameter extraction model is generated by training an algorithm model for processing time sequence data through collection time data samples in advance; inputting the data of the place of charge collection into a second parameter extraction model to obtain the hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data through a place of charge collection data sample in advance; inputting the data of the main payment body into a third parameter extraction model to obtain hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm for processing text type sequence data through a data sample of the main payment body in advance; and performing feature extraction on the to-be-counted collection data, and finishing counting output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model.
In one embodiment, further comprising: and analyzing the extracted features of the merchant payment data and storing the analysis result.
In one embodiment, further comprising: receiving an operation request from a user, wherein the operation request comprises: a payee merchant request, a time of receipt request, and/or a location of receipt request.
In one embodiment, the acquiring the transaction order and the data of the merchant includes: acquiring data in an automatic indexing mode according to a target data source to be acquired; and classifying and cleaning the automatically indexed data according to the data format.
In one embodiment, further comprising: the method comprises the steps of establishing a plurality of cleaning models in advance, wherein the cleaning models are respectively used for cleaning data in different data formats.
In an embodiment, the processing the transaction order and the data of the merchant through the flink distributed computing system, and the obtaining of the payment data to be counted by the merchant includes: analyzing the transaction data through a flink distributed computing system; and carrying out statistics according to an analysis result generated by analyzing the transaction data to obtain the payment data to be counted by the merchant.
In one embodiment, the algorithmic model for processing time series data comprises: a recurrent neural network model, a convolutional neural network model or a long-short term memory network model; the algorithmic model for processing non-sequence data comprises: a deep neural network model, a logistic regression model, a factorization model or a compressed interactive network model; the algorithmic model for processing textual sequence data comprises: a text convolutional neural network model, a cyclic neural network model, a convolutional neural network model, or a long-short term memory network model.
In a second aspect, the disclosed embodiments provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method described above.
In a third aspect, the disclosed embodiments provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method described above when executing the program.
In a fourth aspect, an embodiment of the present disclosure provides a multidimensional parameter statistics apparatus for collected data, where the apparatus includes: the first acquisition module is used for acquiring a transaction order and data of a merchant; the second obtaining module is configured to process the transaction order and the data of the merchant through a flink distributed computing system, and obtain payment data to be counted by the merchant, where the payment data includes: collection time data, collection place data and collection main body data; the third acquisition module is used for inputting the collection time data into a first parameter extraction model and acquiring hidden layer output characteristics of the first parameter extraction model, wherein the first parameter extraction model is generated by training an algorithm model for processing time sequence data through collection time data samples in advance; the fourth acquisition module is used for inputting the data of the place of collection into a second parameter extraction model and acquiring the hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data through a place of collection data sample in advance; the fifth acquisition module is used for inputting the collection subject data into a third parameter extraction model and acquiring hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm model for processing text sequence data through collection subject data samples in advance; and the execution and statistics display module is used for executing and extracting features of the to-be-counted collection data and finishing statistics output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model.
The invention provides a multidimensional parameter statistical method and device of collection data, which are used for acquiring a transaction order and data of a merchant; processing the transaction order and data of the merchant through a flink distributed computing system to obtain the payment data to be counted by the merchant, wherein the payment data comprises: collection time data, collection place data and collection main body data; inputting the collection time data into a first parameter extraction model to obtain hidden layer output characteristics of the first parameter extraction model, wherein the first parameter extraction model is generated by training an algorithm model for processing time sequence data through collection time data samples in advance; inputting the data of the place of charge collection into a second parameter extraction model to obtain the hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data through a place of charge collection data sample in advance; inputting the data of the main payment body into a third parameter extraction model to obtain hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm model for processing text type sequence data through a data sample of the main payment body in advance; and performing feature extraction on the to-be-counted collection data, and finishing counting output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model. The method can safely, conveniently and accurately complete the statistical operation of the collection data, and provides usability for subsequent analysis and decision-making based on the collection data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments are briefly introduced as follows:
FIG. 1 is a flow chart illustrating steps of a method for multidimensional parameter statistics of collected data according to an embodiment of the present invention; and
fig. 2 is a schematic structural diagram of a multidimensional parameter statistics apparatus for collecting data according to an embodiment of the present invention.
Detailed Description
The present application will now be described in further detail with reference to the accompanying drawings and examples.
In the following description, the terms "first" and "second" are used for descriptive purposes only and are not intended to indicate or imply relative importance. The following description provides embodiments of the disclosure, which may be combined or substituted for various embodiments, and this application is therefore intended to cover all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then this application should also be considered to include an embodiment that includes one or more of all other possible combinations of A, B, C, D, even though this embodiment may not be explicitly recited in text below.
In order to make the objects, technical solutions and advantages of the present invention more clearly understood, the following describes in detail a specific implementation of the method and apparatus for collecting data multidimensional parameter statistics according to the present invention by way of example and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, which is a schematic flow chart of a multidimensional parameter statistical method of collected data in an embodiment, specifically includes the following steps:
the method for counting the multidimensional parameters of the collected data is applied to electronic equipment, a double-sided asynchronous liquid crystal display screen assembly is arranged on the electronic equipment, and the double-sided asynchronous liquid crystal display screen assembly comprises a display screen, a 3D structure optical identification reading module and a positioning module. Specifically, the display screen include the screen apron and with the screen apron looks range upon range of light-emitting module, the screen apron is equipped with light transmission district and non-light transmission district, the display screen shows the image through light transmission district, the light-emitting module is equipped with main part and kink, the main part has the orientation go out the plain noodles in light transmission district, it is parallel to go out the plain noodles the screen apron, the kink connect in the main part is close to the edge in non-light transmission district, the kink is towards keeping away from the direction hunch-up of screen apron, with non-light transmission district forms the interval.
In addition, 3D structure light identification reading module includes two-dimensional code input module and the two-dimensional code processing module who links to each other with two-dimensional code input module. Specifically, the two-dimensional code input module comprises an image position sensor chip, and a touch signal output circuit and an image position sensor circuit which are electrically connected to the image position sensor chip; in addition, the two-dimensional code processing module comprises an image position processing chip, an image module circuit and an external interface circuit, wherein the image module circuit and the external interface circuit are electrically connected to the image position processing chip.
Furthermore, the positioning module comprises a communication data antenna and a control circuit, wherein the data communication antenna is arranged on the outer surface of the electronic equipment body through a sliding block, the sliding block is in sliding connection with the outer surface of the electronic equipment body through a sliding groove, the data communication antenna is in sliding connection with the sliding block through a ratchet mechanism, the data communication antenna is electrically connected with the control circuit, the control circuit is embedded in the electronic equipment body, and the positioning module comprises a positioning mechanism, a GNSS positioning device, a GPRS wireless data communication positioning device, a WiFi AP wireless communication positioning device, a network IP positioning device, a wireless data communication positioning device, a wireless access point (WiFi AP) wireless communication positioning device, a wireless Access Point (AP) wireless communication positioning device, a wireless, The network IP positioning devices are connected in parallel and are respectively and electrically connected with the data communication bus through the data cache circuit, the data communication bus is respectively and electrically connected with the data communication antenna and the driving circuit through the data cache circuit, and the control circuit is electrically connected with the electronic equipment body circuit.
Specifically, the acquiring of the transaction order and the data of the merchant includes:
acquiring data in an automatic indexing mode according to a target data source to be acquired; and classifying and cleaning the automatically indexed data according to the data format. Furthermore, it should be noted that, in an embodiment, the multi-dimensional parameter statistical method for collected data according to the present disclosure further includes: the method comprises the steps of establishing a plurality of cleaning models in advance, wherein the cleaning models are respectively used for cleaning data in different data formats. Therefore, the accuracy and the usability of obtaining the transaction orders and the data of the merchants are improved.
In one embodiment, the present disclosure relates to a multidimensional parameter statistical method for collected data, further comprising: receiving an operation request from a user, wherein the operation request comprises: a payee merchant request, a time of receipt request, and/or a location of receipt request. Therefore, the accuracy of information acquisition is improved.
102, processing a transaction order and data of a merchant through a flink distributed computing system, and acquiring payment data to be counted by the merchant, wherein the payment data comprises: collection time data, collection location data and collection subject data.
Specifically, the processing the transaction order and the data of the merchant through the flink distributed computing system to obtain the payment data to be counted by the merchant includes: analyzing the transaction data through a flink distributed computing system; and carrying out statistics according to an analysis result generated by analyzing the transaction data to obtain the payment data to be counted by the merchant. Therefore, the accuracy of obtaining the payment data to be counted by the merchant is improved.
It should be noted that the algorithm model for processing the time series data includes: a recurrent neural network model, a convolutional neural network model, or a long-short term memory network model. Therefore, the diversity, flexibility and usability of the algorithm are improved.
And 104, inputting the data of the point of collection into a second parameter extraction model to obtain the hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data through a point of collection data sample in advance.
It should be noted that the algorithm model for processing non-sequence data includes: a deep neural network model, a logistic regression model, a factorization model, or a compressed interactive network model. Therefore, the diversity, flexibility and usability of the algorithm are improved.
And 105, inputting the collection subject data into a third parameter extraction model to obtain hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm model for processing text sequence data through collection subject data samples in advance.
It should be noted that the algorithm model for processing the text-based sequence data includes: a text convolutional neural network model, a cyclic neural network model, a convolutional neural network model, or a long-short term memory network model. Therefore, the diversity, flexibility and usability of the algorithm are improved.
And 106, performing feature extraction on the to-be-counted collection data and finishing counting output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model.
In one embodiment, the present disclosure relates to a multidimensional parameter statistical method for collected data, further comprising: and analyzing the extracted features of the merchant payment data and storing the analysis result. Therefore, the usability of subsequent decision making depending on the result is improved.
The invention provides a multidimensional parameter statistical method of collection data, which comprises the steps of obtaining a transaction order and data of a merchant; processing the transaction order and data of the merchant through a flink distributed computing system to obtain the payment data to be counted by the merchant, wherein the payment data comprises: collection time data, collection place data and collection main body data; inputting the collection time data into a first parameter extraction model to obtain hidden layer output characteristics of the first parameter extraction model, wherein the first parameter extraction model is generated by training an algorithm model for processing time sequence data through collection time data samples in advance; inputting the data of the place of charge collection into a second parameter extraction model to obtain the hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data through a place of charge collection data sample in advance; inputting the data of the main payment body into a third parameter extraction model to obtain hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm model for processing text type sequence data through a data sample of the main payment body in advance; and performing feature extraction on the to-be-counted collection data, and finishing counting output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model. The method can safely, conveniently and accurately complete the statistical operation of the collection data, and provides usability for subsequent analysis and decision-making based on the collection data.
Based on the same invention concept, the invention also provides a multi-dimensional parameter statistical device of the collection data. Because the principle of solving the problems of the device is similar to the multi-dimensional parameter statistical method of the collection data, the implementation of the device can be realized according to the specific steps of the method, and repeated parts are not repeated.
Fig. 2 is a schematic structural diagram of a multidimensional parameter statistics apparatus for collecting data in an embodiment. The multidimensional parameter statistic device 10 for collection data comprises: a first obtaining module 100, a second obtaining module 200, a third obtaining module 300, a fourth obtaining module 400, a fifth obtaining module 500 and an execution and statistics display module 600.
The first obtaining module 100 is configured to obtain a transaction order and data of a merchant; the second obtaining module 200 is configured to process the transaction order and the data of the merchant through a flink distributed computing system, and obtain payment data to be counted by the merchant, where the payment data includes: collection time data, collection place data and collection main body data; the third obtaining module 300 is configured to input the collection time data into a first parameter extraction model, and obtain a hidden layer output feature of the first parameter extraction model, where the first parameter extraction model is generated by training an algorithm model for processing time series data in advance through collection time data samples; the fourth obtaining module 400 is configured to input the payee location data into a second parameter extraction model, and obtain a hidden layer output feature of the second parameter extraction model, where the second parameter extraction model is generated by training an algorithm model for processing non-sequence data in advance through a payee location data sample; the fifth obtaining module 500 is configured to input the collection subject data into a third parameter extraction model, and obtain hidden layer output features of the third parameter extraction model, where the third parameter extraction model is generated by training an algorithm model for processing text sequence data in advance through collection subject data samples; the execution and statistics display module 600 is configured to execute feature extraction on the to-be-counted collection data and complete statistics output display, where the extracted features include a hidden layer output feature of the first parameter extraction model, a hidden layer output feature of the second parameter extraction model, and a hidden layer output feature of the third parameter extraction model.
The invention provides a multidimensional parameter statistical device of collection data, which comprises the steps of firstly, acquiring a transaction order and data of a merchant through a first acquisition module; and processing the transaction order and the data of the merchant through a flink distributed computing system by a second acquisition module to acquire the payment data to be counted by the merchant, wherein the payment data comprises: collection time data, collection place data and collection main body data; inputting the collection time data into a first parameter extraction model by a third acquisition module to acquire hidden layer output characteristics of the first parameter extraction model, wherein the first parameter extraction model is generated by training an algorithm model for processing time sequence data through collection time data samples in advance; inputting the data of the place of collection into a second parameter extraction model by a fourth acquisition module to acquire hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data in advance through a place of collection data sample; inputting the money receiving main data into a third parameter extraction model by a fifth acquisition module to acquire hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm model for processing text sequence data through money receiving main data samples in advance; and finally, executing and extracting features of the to-be-counted collection data through an executing and counting display module, and finishing counting output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model. The device can safely, conveniently and accurately complete the statistical operation of the collection data, and provides usability for subsequent analysis and decision-making based on the collection data.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by the processor in fig. 1.
The embodiment of the invention also provides a computer program product containing the instruction. Which when run on a computer causes the computer to perform the method of fig. 1 described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
Also, as used herein, the use of "or" in a list of items beginning with "at least one" indicates a separate list, e.g., "A, B or at least one of C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the described example is preferred or better than other examples.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. The utility model provides a multidimension degree parameter statistical method of collection data, is applied to electronic equipment, its characterized in that, be provided with two-sided asynchronous liquid crystal display subassembly on the electronic equipment, two-sided asynchronous liquid crystal display subassembly includes display screen, 3D structure light and reads module and location module, the method includes:
acquiring a transaction order and data of a merchant;
processing the transaction order and data of the merchant through a flink distributed computing system to obtain the payment data to be counted by the merchant, wherein the payment data comprises: collection time data, collection place data and collection main body data;
inputting the collection time data into a first parameter extraction model to obtain hidden layer output characteristics of the first parameter extraction model, wherein the first parameter extraction model is generated by training an algorithm model for processing time sequence data through collection time data samples in advance;
inputting the data of the place of charge collection into a second parameter extraction model to obtain the hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data through a place of charge collection data sample in advance;
inputting the data of the main payment body into a third parameter extraction model to obtain hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm model for processing text type sequence data through a data sample of the main payment body in advance;
and performing feature extraction on the to-be-counted collection data, and finishing counting output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model.
2. The method of claim 1, further comprising the steps of: and analyzing the extracted features of the merchant payment data and storing the analysis result.
3. The method of claim 1, further comprising the steps of: receiving an operation request from a user, wherein the operation request comprises: a payee merchant request, a time of receipt request, and/or a location of receipt request.
4. The method of claim 1, wherein the obtaining transaction orders and data of merchants comprises:
acquiring data in an automatic indexing mode according to a target data source to be acquired; and classifying and cleaning the automatically indexed data according to the data format.
5. The method of claim 4, further comprising the steps of: the method comprises the steps of establishing a plurality of cleaning models in advance, wherein the cleaning models are respectively used for cleaning data in different data formats.
6. The multidimensional parameter statistical method for the payment data as recited in claim 1, wherein the processing of the transaction order and the data of the merchant through the flink distributed computing system to obtain the payment data to be counted by the merchant comprises:
analyzing the transaction data through a flink distributed computing system;
and carrying out statistics according to an analysis result generated by analyzing the transaction data to obtain the payment data to be counted by the merchant.
7. The method of claim 1, wherein the algorithm model for processing time series data comprises: a recurrent neural network model, a convolutional neural network model or a long-short term memory network model;
the algorithmic model for processing non-sequence data comprises: a deep neural network model, a logistic regression model, a factorization model or a compressed interactive network model;
the algorithmic model for processing textual sequence data comprises: a text convolutional neural network model, a cyclic neural network model, a convolutional neural network model, or a long-short term memory network model.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1-7 are implemented when the program is executed by the processor.
10. A multidimensional parameter statistics apparatus for collected data, the apparatus comprising:
the first acquisition module is used for acquiring a transaction order and data of a merchant;
the second obtaining module is configured to process the transaction order and the data of the merchant through a flink distributed computing system, and obtain payment data to be counted by the merchant, where the payment data includes: collection time data, collection place data and collection main body data;
the third acquisition module is used for inputting the collection time data into a first parameter extraction model and acquiring hidden layer output characteristics of the first parameter extraction model, wherein the first parameter extraction model is generated by training an algorithm model for processing time sequence data through collection time data samples in advance;
the fourth acquisition module is used for inputting the data of the place of collection into a second parameter extraction model and acquiring the hidden layer output characteristics of the second parameter extraction model, wherein the second parameter extraction model is generated by training an algorithm model for processing non-sequence data through a place of collection data sample in advance;
the fifth acquisition module is used for inputting the collection subject data into a third parameter extraction model and acquiring hidden layer output characteristics of the third parameter extraction model, wherein the third parameter extraction model is generated by training an algorithm model for processing text sequence data through collection subject data samples in advance;
and the execution and statistics display module is used for executing and extracting features of the to-be-counted collection data and finishing statistics output display, wherein the extracted features comprise hidden layer output features of the first parameter extraction model, hidden layer output features of the second parameter extraction model and hidden layer output features of the third parameter extraction model.
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