CN115273350A - Intelligent tail cabinet verification management method and system based on RFID detection - Google Patents
Intelligent tail cabinet verification management method and system based on RFID detection Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 claims abstract description 23
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
- G07F17/10—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for means for safe-keeping of property, left temporarily, e.g. by fastening the property
- G07F17/12—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for means for safe-keeping of property, left temporarily, e.g. by fastening the property comprising lockable containers, e.g. for accepting clothes to be cleaned
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F9/00—Details other than those peculiar to special kinds or types of apparatus
- G07F9/002—Vending machines being part of a centrally controlled network of vending machines
Abstract
The invention provides an intelligent tail cabinet verification management method and system based on RFID detection, which are applied to the technical field of intelligent detection control, and the method comprises the following steps: and generating a first verification instruction by acquiring the transfer place of the intelligent tail cabinet. And performing primary verification by adopting a primary RFID identification device based on the first verification instruction, and generating a second verification instruction when the primary verification passes. And performing secondary verification through a secondary RFID identification device based on the second verification instruction, and performing related business operation in the trunk cabinet to generate actual operation information when the secondary verification passes. And obtaining the prediction operation information according to the first place and the second place based on the trunk cabinet operation block chain. And verifying the actual operation information by adopting the predicted operation information to obtain an operation verification result. The problem of have among the prior art that the special cabinet that deposits important save article of bank is intelligent lower, lead to the security lower to cause article management confusion easily and lead to the technical problem of article loss is solved.
Description
Technical Field
The invention relates to the technical field of intelligent detection control, in particular to an intelligent tail cabinet verification management method and system based on RFID detection.
Background
The intelligent tail cabinet is an intelligent special cabinet for storing important bank storage articles, and in the existing bank valuable article storage, a safe case or a safe are mostly adopted for valuable article storage, however, the existing safe case or safe is low in intelligence, and only can store articles in encryption modes such as key codes and the like, so that the safety is low, and the problem of article loss caused by disordered article management due to the fact that the articles cannot be known easily is solved.
Consequently, it is lower to have the special cabinet intellectuality of depositing the important save article of bank among the prior art, leads to the lower technical problem who causes article management confusion and lead to the article to lose of security easily.
Disclosure of Invention
The application provides an intelligent tail cabinet verification management method and system based on RFID detection, which are used for solving the technical problem that in the prior art, a special cabinet for storing important stored articles in a bank is low in intelligence, so that the safety is low, and article management is disordered and articles are lost easily.
In view of the above problems, the present application provides an intelligent tail cabinet verification management method and system based on RFID detection.
In a first aspect of the present application, an intelligent tail cabinet verification management method based on RFID detection is provided, and the method is applied to an intelligent tail cabinet verification management system based on RFID detection, the system includes an intelligent tail cabinet, the intelligent tail cabinet includes a cabinet body and a plurality of cabin bodies disposed in the cabinet body, and the method includes: transferring the intelligent tail cabinet from a first location to a second location, wherein the intelligent tail cabinet is movable to a plurality of preset locations, the plurality of preset locations including the first location and the second location; generating a first verification instruction; performing primary verification by adopting a primary RFID identification device based on the first verification instruction to obtain a primary verification result, wherein the primary RFID identification device is arranged in the cabinet body; when the primary verification result is that the verification passes, generating a second verification instruction; performing secondary verification by adopting a plurality of secondary RFID identification devices based on the second verification instruction to obtain a secondary verification result, wherein the plurality of secondary RFID identification devices are respectively arranged in the plurality of bin bodies; when the secondary verification result is that the verification is passed, performing related business operation in the intelligent tail cabinet to generate actual operation information; obtaining predicted operation information according to the first location and the second location based on a trunk cabinet operation block chain; and verifying the actual operation information by adopting the predicted operation information to obtain an operation verification result.
In a second aspect of the present application, an intelligent trunk cabinet verification management system based on RFID detection is provided, the system includes an intelligent trunk cabinet, the intelligent trunk cabinet includes a cabinet body, and a plurality of settings are in the internal storehouse body of cabinet, the system includes: a transfer location obtaining module, configured to transfer the intelligent tail cabinet from a first location to a second location, where the intelligent tail cabinet is movable to a plurality of preset locations, and the plurality of preset locations include the first location and the second location; the primary verification result acquisition module is used for generating a first verification instruction; performing primary verification by adopting a primary RFID identification device based on the first verification instruction to obtain a primary verification result, wherein the primary RFID identification device is arranged in the cabinet body; the second-stage verification result acquisition module is used for generating a second verification instruction when the first-stage verification result is that verification passes; performing secondary verification by adopting a plurality of secondary RFID identification devices based on the second verification instruction to obtain a secondary verification result, wherein the plurality of secondary RFID identification devices are respectively arranged in the plurality of bin bodies; the actual operation information acquisition module is used for performing related business operation in the intelligent tail cabinet to generate actual operation information when the secondary verification result is that the verification passes; the prediction operation information acquisition module is used for acquiring prediction operation information according to the first place and the second place based on the trunk cabinet operation block chain; and the operation verification result acquisition module is used for verifying the actual operation information by adopting the predicted operation information to acquire an operation verification result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method provided by the embodiment of the application, the primary verification and the secondary verification are performed when the intelligent tail cabinet is opened, the environmental parameters are obtained when the primary verification is performed so as to improve the safety when the intelligent tail cabinet is opened, and the secondary verification is performed so as to obtain the articles stored in the tail cabinet so as to judge whether the articles stored in the intelligent tail cabinet are abnormal or not. The operation information is predicted, the predicted operation information and the actual operation information are obtained and verified, so that the operation abnormity is found in time, and the unnecessary waste of human resources caused by the misoperation of workers is further avoided. Solves the problems that the prior art has lower intellectualization of a special cabinet for storing important storage articles of a bank, the lower safety easily causes the confusion of article management and the loss of articles. The safety and the intellectualization of the intelligent tail cabinet are improved, and the technical effect of safety management of articles in the intelligent tail cabinet is facilitated.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart of an intelligent tail cabinet verification management method based on RFID detection according to the present application;
fig. 2 is a schematic flow chart illustrating a primary verification result obtained in an intelligent tail cabinet verification management method based on RFID detection according to the present application;
fig. 3 is a schematic flowchart illustrating a process of acquiring predicted operation information in an intelligent tail cabinet verification management method based on RFID detection according to the present application;
fig. 4 is a schematic structural diagram of an intelligent tail bin verification management system based on RFID detection provided in the present application.
Description of reference numerals: a transfer place acquisition module 11, a primary verification result acquisition module 12, a secondary verification result acquisition module 13, an actual operation information acquisition module 14, a predicted operation information acquisition module 15, and an operation verification result acquisition module 16.
Detailed Description
The application provides an intelligent tail cabinet verification management method and system based on RFID detection, which are used for solving the technical problem that in the prior art, a special cabinet for storing important stored articles in a bank is low in intelligence, so that the safety is low, and article management is disordered and articles are lost easily.
The technical solution in the present application will be described clearly and completely with reference to the accompanying drawings. The described embodiments are only some of the implementations possible in the present application, and not all of the implementations possible in the present application.
Example one
As shown in fig. 1, the present application provides an intelligent tail cabinet verification management method based on RFID detection, which is applied to a hygiene detection system, the hygiene detection system is connected to a detection sensor, an image acquisition device, and a disinfection detection device in communication, and the method includes:
step 100: transferring the intelligent tail cabinet from a first location to a second location, wherein the intelligent tail cabinet is movable to a plurality of preset locations, the plurality of preset locations including the first location and the second location;
specifically, intelligence tail-box cabinet is the special cabinet that is used for depositing the important article of preserving of bank of intellectuality, and intelligence tail-box cabinet includes the cabinet body to and a plurality of setting is in the internal storehouse body of cabinet. And the intelligent tail box cabinet comprises an RFID detection device which is based on radio frequency technology and is used for reading articles stored in the intelligent tail box cabinet, verifying identity information of staff and the like, and reading electronic tag information. Because the intelligent trunk cabinet needs to go to and fro a plurality of bank outlets to store important storage articles, the intelligent trunk cabinet needs to be transferred from a first place to a second place, wherein the first place and the second place are the names of the bank outlets, namely the names of the intelligent trunk cabinet moving to specific positions, the intelligent trunk cabinet can move to a plurality of preset places, the plurality of preset places correspond to the plurality of bank outlets where the intelligent trunk cabinet moves, and the plurality of preset places comprise the first place and the second place.
Step 200: generating a first verification instruction;
step 300: performing primary verification by adopting a primary RFID identification device based on the first verification instruction to obtain a primary verification result, wherein the primary RFID identification device is arranged in the cabinet body;
specifically, because the intelligent tail cabinet needs to be moved between a plurality of banking outlets, that is, preset places, the intelligent tail cabinet needs to be opened when reaching one preset place. When the intelligent tail cabinet is opened, the identity of an opener and other information need to be verified, so that the environment where the intelligent tail cabinet is located is ensured to be a safe environment. And when the intelligent tail bin arrives at a new preset place, generating a first verification instruction, wherein the first verification instruction is used for indicating the system to perform primary verification operation. Wherein, the one-level is verified and is verified through one-level RFID recognition device, wherein one-level RFID recognition device set up in the cabinet is internal, the one-level is verified including verifying the identity of opening personnel, and obtain opening the environmental information around the intelligent tail cabinet, carry out the acquirement of environmental information through sensors such as built-in temperature in the one-level RFID recognition device when acquireing the environmental information around the intelligent tail cabinet, humidity, vibrations sensor and noise sensor, it does not have the anomaly to ensure the personnel of opening of intelligent tail cabinet and open the environment, and then guarantee the security of intelligent tail cabinet when opening.
As shown in fig. 2, the method steps 300 provided in the embodiment of the present application further include:
step 310: acquiring and obtaining multi-dimensional environment parameters by adopting the environment identification unit based on the first verification instruction to obtain an environment parameter set;
step 320: acquiring environmental parameter standards based on the first verification instruction, wherein the environmental parameter standards comprise an environmental vibration standard, an environmental humidity standard, an environmental temperature standard and an environmental noise standard;
step 330: verifying whether the environment parameter set meets the environment parameter standard or not to obtain a primary environment verification result;
step 340: when the primary environment verification result is that the verification is passed, the identity recognition unit is adopted to carry out identity recognition to obtain a primary identity verification result;
step 350: and generating the primary authentication result based on the primary authentication result.
Specifically, the primary RFID device comprises an identity recognition unit for recognizing the identity of an operator and an environment recognition unit for recognizing the environment information around the intelligent tail cabinet. According to the first verification instruction, the environment information around the intelligent tail cabinet is opened through the environment recognition unit for multi-dimensional collection, the environment information including temperature, humidity, noise and vibration is collected, and an environment parameter set is obtained. And then acquiring an environmental parameter standard according to the first verification instruction, wherein the environmental parameter standard comprises an environmental vibration standard, an environmental humidity standard, an environmental temperature standard and an environmental noise standard. Because the opening of the intelligent tail cabinet is mostly at each bank network point, the standard opening environment parameter for opening the intelligent tail cabinet is obtained by obtaining the environment parameter standard, and comparing the environment parameter set with the actually acquired environment parameter set to verify whether the actually acquired environment parameter set meets the environment parameter standard. When the actually acquired environment parameter set meets the environment parameter standard, the first-level environment verification result is that the verification is passed, and at this time, the current environment in which the intelligent tail cabinet is opened meets the commonly used opening environment, and then the next verification operation is carried out. When the actually acquired environment parameter set does not meet the environment parameter standard, the first-level environment verification result is that the verification fails, and at this time, the current environment in which the intelligent tail cabinet is opened does not meet the commonly used opening environment, so that the risk of being robbed or violently dismantled exists, and then danger early warning is performed. And obtaining a primary environment verification result after the environment parameter verification is carried out, wherein the primary environment verification result is used for indicating whether the environment verification is passed or not. When the primary environment verification result is passed, subsequently, the identity recognition unit is adopted to carry out identity recognition on the identity of the opener, and when the identity recognition is carried out, the identity recognition can be carried out through recognition modes such as an identity recognition card, a fingerprint and a face recognition of the opener, so that a primary identity verification result is obtained. And the primary identity authentication result is used for indicating whether the identity authentication of the opener is passed or not. And then generating a primary authentication result based on the primary authentication result, wherein the primary authentication result is used for indicating whether the environment and the authentication pass or not. Through discerning the environmental information and the personnel's identity of opening intelligent tail cabinet, guaranteed the security that intelligent tail cabinet opened.
The method step 330 provided by the embodiment of the present application further includes:
step 331: obtaining a multi-dimensional environmental parameter threshold according to the environmental parameter standard;
step 332: calculating the difference value between the environment parameter set and the multi-dimensional environment parameter threshold value respectively to obtain a multi-dimensional environment parameter difference value;
step 333: carrying out weight distribution based on the importance degree of the multidimensional environment parameter index, and carrying out weighted calculation on the multidimensional environment parameter difference by adopting a weight distribution result to obtain a weighted multidimensional environment parameter difference;
step 334: and obtaining the primary environment verification result according to the weighted multi-dimensional environment parameter difference.
Specifically, a multidimensional environment parameter threshold is set according to the acquired environment parameter standard, because the opening environment in actual operation is not likely to be completely consistent with the environment parameter standard, in order to improve the detection tolerance of the system environment parameter, the environment parameter standard is set to be a range, namely the multidimensional environment parameter threshold, and then whether the environment parameter set meets the environment parameter standard is verified. When the environmental parameter set is verified, the difference value of the environmental parameter set and the multi-dimensional environmental parameter threshold is respectively calculated to obtain the multi-dimensional environmental parameter difference value, because the multi-dimensional environmental parameter threshold is a range value, when elements in the environmental parameter set exceed the maximum range value of the multi-dimensional environmental parameter threshold during calculation, the maximum range value is adopted for difference calculation, when the minimum range value is smaller than the multi-dimensional environmental parameter threshold, the minimum range value is adopted for difference calculation, when the elements in the environmental parameter set fall in the range of the multi-dimensional environmental parameter threshold, the time difference value is 0, the absolute value of the calculation result is obtained, and the calculation result, namely the multi-dimensional environmental parameter difference value is obtained. And then, carrying out weight distribution based on the importance degree of the multidimensional environment parameter index, setting different weight parameters for different environment parameters, carrying out weighting processing on the important environment parameters, so that the influence of the generated environment difference value is larger, otherwise, reducing the weight ratio of the environment influence parameters of the unimportant environment parameters, so that the influence of the generated environment difference value is smaller, and obtaining the weighted multidimensional environment parameter difference value. When the importance degree weight distribution of the multi-dimensional environment parameter index is carried out, the weight distribution can be carried out manually according to the actual environment condition. And then according to the weighted multidimensional environment parameter difference, when the weighted multidimensional environment parameter difference falls into a certain range, the environment verification result is determined to be passed, and when the weighted multidimensional environment parameter difference exceeds the certain range, the environment verification result is determined to be not passed, so that a primary environment verification result is obtained. By obtaining the weighted multi-dimensional environmental parameter difference value, the environmental verification result is evaluated on the whole, the situation that the system fails to judge the environmental verification result due to the fact that the difference value of individual environmental parameters is large is avoided, the accuracy of the system for verifying the environment is improved, and the situation of misjudgment is avoided.
The method step 330 provided by the embodiment of the present application further includes:
step 335: acquiring and acquiring actual position information of the intelligent trunk cabinet based on the environment identification unit;
step 336: acquiring preset position information corresponding to the second place according to the second place;
step 337: and verifying whether the actual position information accords with the preset position information or not to obtain the primary environment verification result.
Specifically, according to the environment recognition unit, the actual position information of the intelligent trunk cabinet is collected. And then acquiring a preset position corresponding to the second location according to the second location. Verifying whether the actual position information of the intelligent tail cabinet accords with preset position information or not, namely judging whether the position of the intelligent tail cabinet accords with the second place reached by transfer or not, when the actual position information accords with the preset position information, indicating that the opening position of the intelligent tail cabinet is abnormal or not, otherwise, when the actual position information does not accord with the preset position information, indicating that the opening position of the intelligent tail cabinet is abnormal or not, and acquiring a primary environment verification result according to the verification result. Through the acquisition to intelligent tail cabinet opening position, judge whether it accords with predetermined position, further improve the security of intelligent tail cabinet.
Step 400: when the primary verification result is that the verification passes, generating a second verification instruction;
step 500: performing secondary verification by adopting a plurality of secondary RFID identification devices based on the second verification instruction to obtain a secondary verification result, wherein the plurality of secondary RFID identification devices are respectively arranged in the plurality of bin bodies;
step 600: when the secondary verification result is that the verification is passed, performing related business operation in the intelligent tail cabinet to generate actual operation information;
specifically, when the primary verification result is that the verification is passed, the environment and the personnel for opening the intelligent tail cabinet are verified to be passed at the moment. At the moment, a second verification instruction is generated and used for controlling the multiple secondary RFID identification devices to perform secondary verification, the stored goods in the bin body of the intelligent trunk cabinet are verified through the multiple secondary RFID identification devices in the secondary verification, and the multiple secondary RFID identification devices are respectively arranged in the bin bodies. When carrying out the transfer between the intelligent tail cabinet bank website, every transfer all need take out or store the inside article of intelligent tail cabinet, in order to avoid causing losing of the inside article of intelligent tail cabinet because article's mistake is got in certain bank website department, consequently need verify the result for verifying the back at the one-level, carry out the second grade and verify the article of the inside storage of intelligent tail cabinet, obtain the second grade and verify the result, the second grade is verified the result and is used for showing whether the article of the inside storage of intelligent tail cabinet exists unusually, then handle on next step when the second grade is verified the result and is passed, it is unusual to explain the article of the inside storage of intelligent tail cabinet when the second grade is verified the result and is not passed, send early warning information this moment. When the second-level verification result is that the verification is passed, the operator is allowed to perform related business operations in the intelligent tail box cabinet, for example, to take or store related articles to be stored, and actual operation information is generated, and the actual operation information can be recorded by the operator and input into the intelligent tail box cabinet solution system.
Step 700: obtaining predicted operation information according to the first location and the second location based on a trunk cabinet operation block chain;
step 800: and verifying the actual operation information by adopting the predicted operation information to obtain an operation verification result.
Specifically, the trunk cabinet operation block chain is used for recording operation information of preset location information on a trunk cabinet, that is, recording information of each bank branch point and operation information of a corresponding trunk cabinet to generate the trunk cabinet operation block chain, and the block chain generates a block once every time the preset location is transferred. And then constructing an operation information prediction model based on the trunk cabinet operation block chain, wherein the operation information prediction model is used for obtaining predicted operation information according to the first place and the second place. And then verifying the actual operation information according to the obtained predicted operation information, and obtaining an operation difference between an operation range of the predicted operation information and the actual operation information when the verification is performed, wherein the operation range of the predicted operation information is taken as an operation range of specific operation, for example, the predicted operation information is cash withdrawal, and the operation range is a range of how much cash needs to be moved, and an operation verification result is obtained according to the operation difference between the predicted operation information and the actual operation information. The operation abnormity is timely found by acquiring the operation verification result, and the unnecessary waste of human resources caused by the misoperation of workers is avoided.
As shown in fig. 3, the method steps 700 provided by the embodiment of the present application further include:
step 710: building block link points at a plurality of preset positions based on the plurality of preset positions;
step 720: acquiring historical operation information for operating the intelligent tail cabinet within historical time in a plurality of preset places to obtain a plurality of historical operation information sets;
step 730: uploading a plurality of historical operation information sets through a plurality of block chain link points to construct and obtain the tail bin operation block chain;
step 740: constructing an operation information prediction model according to the tail cabinet operation block chain;
step 750: and predicting to obtain the predicted operation information according to the first place and the second place by adopting the operation information prediction model.
Specifically, based on a plurality of preset locations, block link points at the plurality of preset locations are constructed, the block link points are used for recording specific trunk cabinet operations at the nodes and generating corresponding blocks. And then, collecting historical operation information of operating the intelligent trunk cabinet at historical time in a plurality of preset places, and acquiring a plurality of historical operation information sets. Uploading a plurality of historical operation information sets through a plurality of block chain link points, and constructing a trunk cabinet operation block chain according to the operation time sequence of the historical operation information. And each block in the trunk cabinet operation block chain comprises preset position information, corresponding trunk cabinet operation information and operation time information. And constructing an operation information prediction model according to the operation block chain of the trunk cabinet. When the operation information prediction model is built, the model is built by acquiring a plurality of historical operation information sets in the tail cabinet operation block chain. Because the historical operation information set comprises the preset position information, the corresponding operation information of the trunk cabinet and the operation time information, the future operation of the preset position can be predicted according to the historical operation information of the preset position. The operation information prediction model predicts the operation information to be predicted based on the first point and the second point.
The method steps 740 provided by the embodiment of the present application further include:
step 741: acquiring a plurality of historical operation information sets according to the tail cabinet operation block chain;
step 742: dividing the plurality of historical operation information sets according to a preset time period to obtain a plurality of sub-historical operation information sets;
step 743: constructing the operation information prediction model based on a BP neural network;
step 744: and carrying out supervision training, verification and testing on the operation information prediction model according to a plurality of sub-historical operation information sets, and if the accuracy of the operation information prediction model meets the preset requirement, constructing and obtaining the operation information prediction model.
Specifically, a plurality of historical operation information sets are obtained according to the tail cabinet operation block chain, and the historical operation information sets comprise preset position information, corresponding tail cabinet operation information and operation time information. And then dividing the plurality of historical operation information sets according to a preset time period to obtain a plurality of sub-historical operation information sets. For example, the time period may be set to be one day or one week, and the historical operation information performed at all preset nodes in a single time period is acquired as a sub-historical operation information set. The sub-historical operation information set comprises preset position information, corresponding trunk cabinet operation information and operation time information. And constructing an operation information prediction model based on the BP neural network according to the obtained multiple sub-historical operation information sets. And taking the preset position information and the operation time information in the multiple sub-historical operation information sets as input data, and taking the corresponding operation information of the trunk cabinet as identification data to perform supervision training on the BP neural network. And verifying and testing the obtained model through a plurality of sub-historical operation information sets until the accuracy of the operation information prediction model output meets a preset requirement, wherein the preset requirement is that the comparison accuracy of the model output data and the corresponding identification data meets a certain threshold value, and then completing construction of the operation information prediction model.
The method step 800 provided by the embodiment of the present application further includes:
step 810: obtaining a preset operation neighborhood of the prediction operation information according to the prediction operation information;
step 820: and verifying whether the actual operation information falls into the operation neighborhood or not to obtain the operation verification result.
Specifically, according to the constructed operation information prediction model, a preset place and operation time are input into the operation information prediction model, and prediction operation information is obtained. And acquiring a neighborhood of the preset operation according to the predicted operation information, wherein the neighborhood of the preset operation comprises a specific operation range, for example, the predicted operation information is cash withdrawal, and the neighborhood of the preset operation is a range of how much cash needs to be moved. And the prediction operation information is stored files, seals and the like, and the neighborhood of the preset operation is the number range of the files and the seals needing to be stored. The prediction operation information forms a conventional operation range by setting the preset operation neighborhood, so that misoperation of workers is avoided, and misoperation is found in time. And verifying whether the actual operation information falls into the operation neighborhood, wherein the actual operation information is normal operation when the actual operation information falls into the operation neighborhood, otherwise, the actual operation information is abnormal operation, and a final operation verification result is obtained.
To sum up, the method provided by the embodiment of the present application generates the first verification instruction by obtaining the transfer location of the intelligent tail cabinet. And performing primary verification by adopting a primary RFID identification device based on the first verification instruction, and generating a second verification instruction when the primary verification passes. And performing secondary verification through a secondary RFID identification device based on the second verification instruction, and performing related business operation in the trunk cabinet to generate actual operation information when the secondary verification passes. And obtaining the prediction operation information according to the first place and the second place based on the trunk cabinet operation block chain. And verifying the actual operation information by adopting the predicted operation information to obtain an operation verification result. The problem of have among the prior art to deposit the special cabinet intellectuality of important save article of bank lower, lead to the security lower to cause article management confusion to lead to the technical problem of article loss easily. The safety and the intellectualization of the intelligent tail cabinet are improved, and the technical effect of safety management on the articles in the intelligent tail cabinet is facilitated.
Example two
Based on the same inventive concept as the method for managing the verification of the intelligent tail cabinet based on the RFID detection in the foregoing embodiment, as shown in fig. 4, the present application provides an intelligent tail cabinet verification management system based on the RFID detection, where the system includes an intelligent tail cabinet, the intelligent tail cabinet includes a cabinet body and a plurality of cabin bodies arranged in the cabinet body, and the system includes:
a transfer location obtaining module 11, configured to transfer the intelligent tail cabinet from a first location to a second location, where the intelligent tail cabinet is movable to a plurality of preset locations, and the plurality of preset locations includes the first location and the second location;
a primary verification result obtaining module 12, configured to generate a first verification instruction; performing primary verification by adopting a primary RFID identification device based on the first verification instruction to obtain a primary verification result, wherein the primary RFID identification device is arranged in the cabinet body;
a secondary verification result obtaining module 13, configured to generate a second verification instruction when the primary verification result is that verification passes; performing secondary verification by adopting a plurality of secondary RFID identification devices based on the second verification instruction to obtain a secondary verification result, wherein the plurality of secondary RFID identification devices are respectively arranged in the plurality of bin bodies;
the actual operation information acquisition module 14 is configured to perform relevant business operations in the intelligent tail box cabinet to generate actual operation information when the secondary verification result is that the verification passes;
the prediction operation information obtaining module 15 is configured to obtain prediction operation information according to the first location and the second location based on the trunk cabinet operation block chain;
and an operation verification result obtaining module 16, configured to verify the actual operation information by using the predicted operation information, and obtain an operation verification result.
Further, the primary verification result obtaining module 12 is further configured to:
acquiring and acquiring multi-dimensional environment parameters by adopting the environment identification unit based on the first verification instruction to acquire an environment parameter set;
acquiring environmental parameter standards based on the first verification instruction, wherein the environmental parameter standards comprise an environmental vibration standard, an environmental humidity standard, an environmental temperature standard and an environmental noise standard;
verifying whether the environment parameter set meets the environment parameter standard or not to obtain a primary environment verification result;
when the primary environment verification result is that the verification is passed, the identity recognition unit is adopted to carry out identity recognition to obtain a primary identity verification result;
and generating the primary authentication result based on the primary authentication result.
Further, the primary verification result obtaining module 12 is further configured to:
obtaining a multi-dimensional environmental parameter threshold according to the environmental parameter standard;
calculating the difference value between the environment parameter set and the multi-dimensional environment parameter threshold value respectively to obtain a multi-dimensional environment parameter difference value;
carrying out weight distribution based on the importance degree of the multidimensional environment parameter index, and carrying out weighted calculation on the multidimensional environment parameter difference by adopting a weight distribution result to obtain a weighted multidimensional environment parameter difference;
and obtaining the primary environment verification result according to the weighted multi-dimensional environment parameter difference.
Further, the primary verification result obtaining module 12 is further configured to:
acquiring and acquiring actual position information of the intelligent trunk cabinet based on the environment identification unit;
acquiring preset position information corresponding to the second place according to the second place;
and verifying whether the actual position information accords with the preset position information or not, and obtaining the primary environment verification result.
Further, the prediction operation information obtaining module 15 is further configured to:
building block link points at a plurality of preset positions based on the plurality of preset positions;
acquiring historical operation information for operating the intelligent tail cabinet within historical time in a plurality of preset places to obtain a plurality of historical operation information sets;
uploading a plurality of historical operation information sets through a plurality of block chain link points to construct and obtain the tail bin operation block chain;
constructing and obtaining an operation information prediction model according to the tail cabinet operation block chain;
and predicting to obtain the predicted operation information according to the first place and the second place by adopting the operation information prediction model.
Further, the prediction operation information obtaining module 15 is further configured to:
acquiring a plurality of historical operation information sets according to the tail cabinet operation block chain;
dividing the plurality of historical operation information sets according to a preset time period to obtain a plurality of sub-historical operation information sets;
constructing the operation information prediction model based on a BP neural network;
and carrying out supervision training, verification and testing on the operation information prediction model according to a plurality of sub-historical operation information sets, and if the accuracy of the operation information prediction model meets the preset requirement, constructing and obtaining the operation information prediction model.
Further, the operation verification result obtaining module 16 is further configured to:
obtaining a preset operation neighborhood of the prediction operation information according to the prediction operation information;
and verifying whether the actual operation information falls into the operation neighborhood or not to obtain the operation verification result.
The second embodiment is used for executing the method as in the first embodiment, and both the execution principle and the execution basis can be obtained through the content recorded in the first embodiment, which is not described herein again. Although the present application has been described in connection with particular features and embodiments thereof, the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application, and the content thus obtained also falls within the scope of protection of the present application.
Claims (8)
1. An intelligent tail cabinet verification management method based on RFID detection is applied to an intelligent tail cabinet verification management system based on RFID detection, the system comprises an intelligent tail cabinet, the intelligent tail cabinet comprises a cabinet body and a plurality of cabin bodies arranged in the cabinet body, and the method comprises the following steps:
transferring the intelligent tail cabinet from a first location to a second location, wherein the intelligent tail cabinet is movable to a plurality of preset locations, the plurality of preset locations including the first location and the second location;
generating a first verification instruction;
performing primary verification by adopting a primary RFID identification device based on the first verification instruction to obtain a primary verification result, wherein the primary RFID identification device is arranged in the cabinet body;
when the primary verification result is that the verification passes, generating a second verification instruction;
performing secondary verification by adopting a plurality of secondary RFID identification devices based on the second verification instruction to obtain a secondary verification result, wherein the plurality of secondary RFID identification devices are respectively arranged in the plurality of bin bodies;
when the secondary verification result is that the verification is passed, performing related business operation in the intelligent tail cabinet to generate actual operation information;
obtaining predicted operation information according to the first location and the second location based on a trunk cabinet operation block chain;
and verifying the actual operation information by adopting the predicted operation information to obtain an operation verification result.
2. The method of claim 1, wherein the primary RFID identification device includes an identity recognition unit and an environment recognition unit, the method comprising:
acquiring and acquiring multi-dimensional environment parameters by adopting the environment identification unit based on the first verification instruction to acquire an environment parameter set;
acquiring environmental parameter standards based on the first verification instruction, wherein the environmental parameter standards comprise an environmental vibration standard, an environmental humidity standard, an environmental temperature standard and an environmental noise standard;
verifying whether the environment parameter set meets the environment parameter standard or not to obtain a primary environment verification result;
when the primary environment verification result is that the verification is passed, the identity recognition unit is adopted to carry out identity recognition to obtain a primary identity verification result;
and generating the primary authentication result based on the primary authentication result.
3. The method of claim 2, wherein verifying whether the set of environmental parameters meets the environmental parameter criteria comprises:
obtaining a multi-dimensional environmental parameter threshold according to the environmental parameter standard;
calculating the difference value between the environment parameter set and the multi-dimensional environment parameter threshold value respectively to obtain a multi-dimensional environment parameter difference value;
carrying out weight distribution based on the importance degree of the multidimensional environment parameter index, and carrying out weighted calculation on the multidimensional environment parameter difference by adopting a weight distribution result to obtain a weighted multidimensional environment parameter difference;
and obtaining the primary environment verification result according to the weighted multi-dimensional environment parameter difference.
4. The method of claim 2, wherein verifying whether the set of environmental parameters meets the environmental parameter criteria further comprises:
acquiring and acquiring actual position information of the intelligent trunk cabinet based on the environment identification unit;
acquiring preset position information corresponding to the second place according to the second place;
and verifying whether the actual position information accords with the preset position information or not, and obtaining the primary environment verification result.
5. The method of claim 1, wherein obtaining predicted operational information from the first location and the second location based on an end bin operational block chain comprises:
building block link points at a plurality of preset positions based on the plurality of preset positions;
acquiring historical operation information for operating the intelligent tail cabinet within historical time in a plurality of preset places to obtain a plurality of historical operation information sets;
uploading a plurality of historical operation information sets through a plurality of block chain link points to construct and obtain the tail bin operation block chain;
constructing and obtaining an operation information prediction model according to the tail cabinet operation block chain;
and predicting to obtain the predicted operation information according to the first place and the second place by adopting the operation information prediction model.
6. The method of claim 5, wherein constructing a get operational information prediction model from the chain of tail bin operational blocks comprises:
acquiring a plurality of historical operation information sets according to the tail cabinet operation block chain;
dividing the plurality of historical operation information sets according to a preset time period to obtain a plurality of sub-historical operation information sets;
constructing the operation information prediction model based on a BP neural network;
and carrying out supervision training, verification and testing on the operation information prediction model according to a plurality of sub-historical operation information sets, and if the accuracy of the operation information prediction model meets a preset requirement, constructing and obtaining the operation information prediction model.
7. The method of claim 1, wherein verifying the actual operation information using the predicted operation information comprises:
obtaining a preset operation neighborhood of the prediction operation information according to the prediction operation information;
and verifying whether the actual operation information falls into the operation neighborhood or not to obtain the operation verification result.
8. The utility model provides an intelligence boot cabinet verifies management system based on RFID detects, its characterized in that, the system includes intelligence boot cabinet, intelligence boot cabinet includes the cabinet body to and a plurality of setting is in the internal storehouse body of cabinet, the system includes:
a transfer location obtaining module, configured to transfer the intelligent tail cabinet from a first location to a second location, where the intelligent tail cabinet is movable to a plurality of preset locations, and the plurality of preset locations include the first location and the second location;
the primary verification result acquisition module is used for generating a first verification instruction; performing primary verification by adopting a primary RFID identification device based on the first verification instruction to obtain a primary verification result, wherein the primary RFID identification device is arranged in the cabinet body;
the second-stage verification result acquisition module is used for generating a second verification instruction when the first-stage verification result is that verification passes; performing secondary verification by adopting a plurality of secondary RFID identification devices based on the second verification instruction to obtain a secondary verification result, wherein the plurality of secondary RFID identification devices are respectively arranged in the plurality of bin bodies;
the actual operation information acquisition module is used for performing related business operation in the intelligent tail cabinet to generate actual operation information when the secondary verification result is that the verification passes;
the prediction operation information acquisition module is used for acquiring prediction operation information according to the first place and the second place based on a trunk cabinet operation block chain;
and the operation verification result acquisition module is used for verifying the actual operation information by adopting the predicted operation information to acquire an operation verification result.
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