Financial payment verification system

Abstract

The invention relates to a financial payment verification system which comprises a depth-of-field analysis mechanism, a data acquisition mechanism, an information capture mechanism, a learning conversion mechanism, a content authentication component and an enabling control component. According to the invention, the security of the collection process can be improved, and an invalid collection process is avoided.

Description

Technical Field

[0001] The present invention relates to the field of financial payment, and in particular to a financial payment verification system. Background Art

[0002] Financial payment is an important part of financial transactions, covering the process of transferring funds from one party to another. In modern economic activities, financial payments are usually made electronically, such as online bank transfers, mobile payments, credit card payments, etc. This payment method greatly simplifies the traditional cash transaction process and improves the efficiency and security of fund transactions. There are many different financial payment methods, which are constantly innovating with the development of technology. Currently, common payment methods include online payment, mobile payment, and e-wallet payment. Online payment is the transfer of funds through channels such as online banking; mobile payment is the payment behavior realized through mobile phone applications; and e-wallet payment is the completion of fund transactions through specific e-wallet tools. These payment methods all play an important role in daily life and business activities.

[0003] When financial payment is upgraded to be intelligent, it is necessary to detect whether there is a payment terminal on site whose appearance structure and appearance color match the standard appearance structure and standard appearance color of the financial payment terminal when the financial payment is initiated. Only when it exists, the code body scanning function of the code body scanning terminal is enabled, otherwise, the code body scanning function of the code body scanning terminal is disabled, thereby improving the security of the payment process and avoiding entering an invalid payment process. Obviously, there is a lack of corresponding solutions in the prior art. Summary of the invention

[0004] In order to solve the technical problems in the related fields, the present invention provides a financial payment verification system, which introduces a depth of field analysis mechanism for when there is a target with a depth of field value less than or equal to a preset depth of field threshold in the real-time shooting picture, and uses the imaging area of ​​the target with a depth of field value less than or equal to the preset depth of field threshold in the real-time shooting picture as a reference imaging area where a financial payment terminal is suspected to exist for subsequent dual identification of appearance and color, wherein the real-time shooting picture comes from a micro-shooting mechanism embedded in the front panel body of a code body scanning terminal for scanning a payment code, and the code body scanning terminal is used to start the payment process to the corresponding account of the financial payment terminal when scanning the payment code displayed by the financial payment terminal, and also introduces a learning conversion mechanism for performing multiple learning operations on the BP neural network to obtain the BP neural network after completing multiple learning operations and output it as a BP neural network model, the number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture, thereby completing the structural design of the BP neural network model, and using the BP neural network model according to the reference imaging The number of occupied pixels in the area, the number of edge pixels, the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to each pixel, the hue component values ​​corresponding to each pixel in the reference imaging area, the brightness component values ​​corresponding to each pixel in the reference imaging area, and the saturation component values ​​corresponding to each pixel in the reference imaging area are intelligently identified whether the target with a depth of field value less than or equal to a preset depth of field threshold in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in both appearance structure and appearance color, and an enabling control component is introduced to enable the code body scanning function of the code body scanning terminal when the BP neural network model intelligently identifies that the target with a depth of field value less than or equal to the preset depth of field threshold in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in both appearance structure and appearance color. Otherwise, the code body scanning function of the code body scanning terminal is disabled, thereby improving the security of the payment collection process and avoiding entering an invalid payment collection process.

[0005] According to the present invention, a financial payment verification system is provided, the system comprising: A depth of field analysis mechanism, configured to output, when there is a target with a depth of field value less than or equal to a preset depth of field threshold in a real-time shooting picture, an imaging area of ​​the target with a depth of field value less than or equal to the preset depth of field threshold in the real-time shooting picture as a reference imaging area, wherein the real-time shooting picture comes from a micro-shooting mechanism embedded in a front panel body of a code body scanning terminal for scanning a payment code; A data acquisition mechanism, connected to the depth of field analysis mechanism, for acquiring the number of occupied pixels, the number of edge pixels, and the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to the respective pixels in the reference imaging area; An information capturing mechanism, connected to the depth of field analyzing mechanism, for capturing respective hue component values ​​corresponding to respective pixels of the reference imaging area, respective brightness component values ​​corresponding to respective pixels of the reference imaging area, and respective saturation component values ​​corresponding to respective pixels of the reference imaging area; A learning conversion mechanism, used for performing multiple learning operations on the BP neural network to obtain a BP neural network after completing multiple learning operations and outputting it as a BP neural network model, wherein the number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture; The content identification component is connected to the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, and is used to use a BP neural network model to intelligently identify whether the target with a depth of field value less than or equal to a preset depth of field threshold value in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in terms of both appearance structure and appearance color according to the number of occupied pixels in the reference imaging area, the number of edge pixels, the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to each pixel, the hue component values ​​corresponding to each pixel in the reference imaging area, the brightness component values ​​corresponding to each pixel in the reference imaging area, and the saturation component values ​​corresponding to each pixel in the reference imaging area; An enabling control component is connected to the content identification component and is used to enable the code body scanning function of the code body scanning terminal when the BP neural network model intelligently identifies that the target with a depth of field value less than or equal to a preset depth of field threshold in the real-time shooting picture matches the standard shape structure and standard shape color of the financial payment terminal in terms of both shape structure and shape color; otherwise, the code body scanning function of the code body scanning terminal is disabled.

[0006] It can be seen that the present invention has at least the following four key invention points: Firstly: a depth of field analysis mechanism is introduced for when there is a target with a depth of field value less than or equal to a preset depth of field threshold in a real-time shooting picture, the imaging area of ​​the target with a depth of field value less than or equal to the preset depth of field threshold in the real-time shooting picture is used as a reference imaging area where a financial payment terminal is suspected to exist for subsequent appearance and color dual identification, wherein the real-time shooting picture comes from a micro-shooting mechanism embedded in a front panel body of a code body scanning terminal for scanning a payment code, and the code body scanning terminal is used to start a payment process to a corresponding account of the financial payment terminal when scanning a payment code displayed by the financial payment terminal; Secondly: introducing a learning conversion mechanism to perform multiple learning operations on the BP neural network to obtain a BP neural network after completing multiple learning operations and outputting it as a BP neural network model. The number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture, thereby completing the structural design of the BP neural network model; Thirdly: adopting the BP neural network model to intelligently identify whether the target with a depth of field value less than or equal to a preset depth of field threshold value in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in terms of both appearance structure and appearance color according to the number of occupied pixels in the reference imaging area, the number of edge pixels, the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to each pixel, the hue component values ​​corresponding to each pixel in the reference imaging area, the brightness component values ​​corresponding to each pixel in the reference imaging area, and the saturation component values ​​corresponding to each pixel in the reference imaging area; Finally: an enabling control component is introduced to enable the code body scanning function of the code body scanning terminal when the BP neural network model intelligently identifies that the target with a depth of field value less than or equal to a preset depth of field threshold in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in terms of both appearance structure and appearance color. Otherwise, the code body scanning function of the code body scanning terminal is disabled, thereby improving the security of the payment process and avoiding entering an invalid payment process. DETAILED DESCRIPTION

[0007] The implementation scheme of the financial payment verification system of the present invention will be described in detail below.

[0008] The financial payment verification system shown in embodiment A of the present invention includes: A depth of field analysis mechanism, configured to output, when there is a target with a depth of field value less than or equal to a preset depth of field threshold in a real-time shooting picture, an imaging area of ​​the target with a depth of field value less than or equal to the preset depth of field threshold in the real-time shooting picture as a reference imaging area, wherein the real-time shooting picture comes from a micro-shooting mechanism embedded in a front panel body of a code body scanning terminal for scanning a payment code; Specifically, the depth of field analysis mechanism is used to output the imaging area of ​​the target whose depth of field value is less than or equal to the preset depth of field threshold in the real-time shooting picture as the reference imaging area when there is a target whose depth of field value is less than or equal to the preset depth of field threshold in the real-time shooting picture, wherein the real-time shooting picture is from the micro-shooting mechanism embedded in the front panel body of the code body scanning terminal for scanning the payment code, and includes: the content data cache component of the depth of field analysis mechanism, which is used to cache the preset depth of field threshold; A data acquisition mechanism, connected to the depth of field analysis mechanism, for acquiring the number of occupied pixels, the number of edge pixels, and the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to the respective pixels in the reference imaging area; An information capturing mechanism, connected to the depth of field analyzing mechanism, for capturing respective hue component values ​​corresponding to respective pixels of the reference imaging area, respective brightness component values ​​corresponding to respective pixels of the reference imaging area, and respective saturation component values ​​corresponding to respective pixels of the reference imaging area; A learning conversion mechanism, used for performing multiple learning operations on the BP neural network to obtain a BP neural network after completing multiple learning operations and outputting it as a BP neural network model, wherein the number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture; The content identification component is connected to the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, and is used to use a BP neural network model to intelligently identify whether the target with a depth of field value less than or equal to a preset depth of field threshold value in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in terms of both appearance structure and appearance color according to the number of occupied pixels in the reference imaging area, the number of edge pixels, the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to each pixel, the hue component values ​​corresponding to each pixel in the reference imaging area, the brightness component values ​​corresponding to each pixel in the reference imaging area, and the saturation component values ​​corresponding to each pixel in the reference imaging area; an enabling control component connected to the content identification component, for enabling the code body scanning function of the code body scanning terminal when the BP neural network model intelligently identifies that the target with a depth of field value less than or equal to a preset depth of field threshold in the real-time shooting picture matches the standard shape structure and standard shape color of the financial payment terminal in terms of both shape structure and shape color, otherwise, disabling the code body scanning function of the code body scanning terminal; Among them, the BP neural network model is used to intelligently identify whether the target with a depth of field value less than or equal to a preset depth of field threshold value existing in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in both appearance structure and appearance color according to the number of occupied pixels in the reference imaging area, the number of edge pixels, the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to each pixel, the hue component values ​​corresponding to each pixel in the reference imaging area, the brightness component values ​​corresponding to each pixel in the reference imaging area, and the saturation component values ​​corresponding to each pixel in the reference imaging area. The BP neural network model includes: the BP neural network model outputs a terminal identification mark, which is used to indicate whether the target with a depth of field value less than or equal to the preset depth of field threshold value existing in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in both appearance structure and appearance color; Among them, multiple learning operations are performed on the BP neural network to obtain the BP neural network after completing multiple learning operations and output it as a BP neural network model, and the number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture, including: using a signal conversion function to represent the signal conversion relationship between the number of learning operations completed by the BP neural network and the resolution of the real-time shooting picture.

[0009] Compared to the embodiment A of the present invention, the financial payment verification system shown in the embodiment B of the present invention may also include: A data analysis component, connected to the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, respectively, for measuring the decibels of noise near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively; Wherein, the data analysis component is connected with the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively, and is used to measure the decibels of the noise near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively, including: the data analysis component includes a plurality of decibel measurement units, which are used to be connected with the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively, so as to complete the measurement of the decibels of the noise near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively; Wherein, the data analysis component includes a plurality of decibel measurement units, which are used to be connected to the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively, so as to complete the respective measurement of the decibel of the nearby noise of the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively, including: the plurality of decibel measurement units are a plurality of decibel sensor circuits, which are used to be connected to the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively, so as to complete the respective measurement of the decibel of the nearby noise of the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively; Wherein, the plurality of decibel measurement units are a plurality of decibel sensing circuits, which are used to be connected to the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, so as to complete the respective measurement of the decibel of the nearby noise of the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, including: the structures of the plurality of decibel sensing circuits are the same; Among them, the multiple decibel measurement units are multiple decibel sensor circuits, which are used to connect with the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively to complete the separate measurement of the nearby noise decibels of the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, and also include: the multiple decibel sensor circuits have the same decibel measurement upper limit value and decibel measurement lower limit value.

[0010] Compared to the embodiment A of the present invention, the financial payment verification system shown in the embodiment C of the present invention may also include: A field service component is arranged near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism with the voice control services they need respectively; Wherein, the on-site service component is arranged near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism with the required voice control services respectively, including: the on-site service component includes a sound collection unit, a numerical conversion unit and a signal sending unit; Wherein, the on-site service component is arranged near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism with the required voice control services respectively, and further comprises: the numerical conversion unit is connected to the sound collection unit and the signal issuing unit respectively; And wherein, the on-site service component is arranged near the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism with the voice control services they need respectively. It also includes: the sound acquisition unit is used to collect sound signals near the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism, and whether the sound signals collected respectively belong to sound control signals, so as to only send the sound control signals to the numerical conversion unit for control signal analysis.

[0011] In addition, in the financial payment verification system, multiple learning operations are performed on the BP neural network to obtain the BP neural network after completing multiple learning operations and output it as a BP neural network model, and the number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture, which also includes: in the signal conversion function, the resolution of the real-time shooting picture is the input data, and the number of learning operations completed by the BP neural network is the output data.

[0012] The financial payment verification system of the present invention can intelligently identify whether a target with a depth of field value less than or equal to a preset depth of field threshold in a real-time shooting image matches a financial payment terminal in both appearance structure and appearance color, and only enable the code body scanning function of the code body scanning terminal when it matches, thereby improving the security of the payment process and avoiding entering an invalid payment process, thereby solving the above technical problems.

[0013] While the present invention has been particularly illustrated and described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes in form and details may be made without departing from the spirit and scope of the invention as defined by the claims.

Claims

1. A financial payment verification system, characterized in that: The system comprises: A depth of field analysis mechanism, configured to output, when there is a target with a depth of field value less than or equal to a preset depth of field threshold in a real-time shooting picture, an imaging area of ​​the target with a depth of field value less than or equal to the preset depth of field threshold in the real-time shooting picture as a reference imaging area, wherein the real-time shooting picture comes from a micro-shooting mechanism embedded in a front panel body of a code body scanning terminal for scanning a payment code; A data acquisition mechanism, connected to the depth of field analysis mechanism, for acquiring the number of occupied pixels, the number of edge pixels, and the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to the respective pixels in the reference imaging area; An information capturing mechanism, connected to the depth of field analyzing mechanism, for capturing respective hue component values ​​corresponding to respective pixels of the reference imaging area, respective brightness component values ​​corresponding to respective pixels of the reference imaging area, and respective saturation component values ​​corresponding to respective pixels of the reference imaging area; A learning conversion mechanism, used for performing multiple learning operations on the BP neural network to obtain a BP neural network after completing multiple learning operations and outputting it as a BP neural network model, wherein the number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture; The content identification component is connected to the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, and is used to use a BP neural network model to intelligently identify whether the target with a depth of field value less than or equal to a preset depth of field threshold value in the real-time shooting picture matches the standard appearance structure and standard appearance color of the financial payment terminal in terms of both appearance structure and appearance color according to the number of occupied pixels in the reference imaging area, the number of edge pixels, the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​corresponding to each pixel, the hue component values ​​corresponding to each pixel in the reference imaging area, the brightness component values ​​corresponding to each pixel in the reference imaging area, and the saturation component values ​​corresponding to each pixel in the reference imaging area; An enabling control component is connected to the content identification component and is used to enable the code body scanning function of the code body scanning terminal when the BP neural network model intelligently identifies that the target with a depth of field value less than or equal to a preset depth of field threshold in the real-time shooting picture matches the standard shape structure and standard shape color of the financial payment terminal in terms of both shape structure and shape color; otherwise, the code body scanning function of the code body scanning terminal is disabled.

2. The financial payment verification system according to claim 1, characterized in that: Adopting a BP neural network model to intelligently identify whether a target with a depth of field value less than or equal to a preset depth of field threshold value in a real-time shooting picture matches the standard appearance structure and standard appearance color of a financial payment terminal in terms of both appearance structure and appearance color according to the number of occupied pixels in the reference imaging area, the number of edge pixels, the difference between the maximum depth of field value and the minimum depth of field value among the depth of field values ​​respectively corresponding to each pixel, the hue component values ​​respectively corresponding to each pixel in the reference imaging area, the brightness component values ​​respectively corresponding to each pixel in the reference imaging area, and the saturation component values ​​respectively corresponding to each pixel in the reference imaging area, includes: the BP neural network model outputs a terminal identification mark for indicating whether a target with a depth of field value less than or equal to a preset depth of field threshold value in a real-time shooting picture matches the standard appearance structure and standard appearance color of a financial payment terminal in terms of both appearance structure and appearance color; Among them, multiple learning operations are performed on the BP neural network to obtain the BP neural network after completing multiple learning operations and output it as a BP neural network model, and the number of learning operations completed by the BP neural network is positively correlated with the resolution of the real-time shooting picture, including: using a signal conversion function to represent the signal conversion relationship between the number of learning operations completed by the BP neural network and the resolution of the real-time shooting picture.

3. The financial payment verification system according to claim 2, characterized in that: The system further comprises: A data analysis component, connected to the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, respectively, for measuring the decibels of noise near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism respectively; Among them, the data analysis component is respectively connected with the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, and is used to respectively measure the decibels of noise near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism. The data analysis component includes multiple decibel measurement units, which are respectively connected with the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism to complete the respective measurements of the decibels of noise near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism.

4. The financial payment verification system as claimed in claim 3, characterized in that: The data analysis component includes a plurality of decibel measurement units, which are used to respectively connect with the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism to complete the respective measurements of the decibels of noise near the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism. The plurality of decibel measurement units are a plurality of decibel sensor circuits, which are used to respectively connect with the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism to complete the respective measurements of the decibels of noise near the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism.

5. The financial payment verification system as claimed in claim 4, characterized in that: The multiple decibel measurement units are multiple decibel sensor circuits, which are used to be connected to the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, so as to complete the separate measurement of the nearby noise decibels of the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, including: the structures of the multiple decibel sensor circuits are the same.

6. The financial payment verification system as claimed in claim 5, characterized in that: The multiple decibel measurement units are multiple decibel sensing circuits, which are used to be connected to the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, so as to complete the separate measurement of the decibels of nearby noise of the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism respectively, and also include: the multiple decibel sensing circuits have the same decibel measurement upper limit value and decibel measurement lower limit value.

7. The financial payment verification system as claimed in claim 3, characterized in that: The system further comprises: The on-site service component is arranged near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism with the voice control services they need respectively.

8. The financial payment verification system according to claim 7, characterized in that: The on-site service component is arranged near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism with the voice control services they need respectively, including: the on-site service component includes a sound collection unit, a numerical conversion unit and a signal sending unit.

9. The financial payment verification system as claimed in claim 8, characterized in that: The on-site service component is arranged near the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data collection mechanism and the information capture mechanism with the required voice control services respectively, and further includes: the numerical conversion unit is connected to the sound collection unit and the signal issuing unit respectively; Among them, the on-site service component is arranged near the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism, and is used to provide the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism with the voice control services they need respectively. It also includes: the sound acquisition unit is used to collect sound signals near the content identification component, the learning conversion mechanism, the data acquisition mechanism and the information capture mechanism, and whether the sound signals collected respectively belong to sound control signals, so as to only send the sound control signals to the numerical conversion unit for control signal analysis.