CN116910707B - Model copyright management method and system based on equipment history record - Google Patents
Model copyright management method and system based on equipment history record Download PDFInfo
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Abstract
The invention discloses a model copyright management method and a system based on equipment history record, wherein the method comprises the following steps: responding to an authorization request of target three-dimensional printing equipment for a target model, and acquiring equipment parameters and historical data processing records sent by the target three-dimensional printing equipment; according to the equipment parameters, determining a historical data request record of the target three-dimensional printing equipment from a preset historical database; determining equipment consistency parameters and equipment risk parameters corresponding to the target three-dimensional printing equipment according to the historical data processing record and the historical data request record; and determining whether to authorize the target three-dimensional printing equipment according to the equipment consistency parameter and the equipment risk parameter. Therefore, the invention can achieve safer and finer model copyright management and reduce the possibility of the model being stolen by attack.
Description
Technical Field
The invention relates to the technical field of copyright data processing, in particular to a model copyright management method and system based on equipment history records.
Background
With the development of 3D printing technology, more and more model makers are beginning to be added into the industry of 3D printing, so the problem of copyright protection of model works of the makers is becoming a focus, and with the increase of the number of model files and the continuous emergence of model management platforms, how to improve the authorized security of the models is becoming important.
However, in the prior art, when the copyright management of the model file is realized, the authorization of the device is generally realized only by a conventional encryption and decryption authentication technology, and the recognition accuracy of the device is improved by not considering the full combination of various histories of the device, so that the risk is still higher. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a model copyright management method and a system based on equipment history records, which can achieve safer and finer model copyright management and reduce the possibility of the model being stolen by attack.
In order to solve the technical problem, the first aspect of the present invention discloses a model copyright management method based on a device history record, which comprises the following steps:
responding to an authorization request of target three-dimensional printing equipment for a target model, and acquiring equipment parameters and historical data processing records sent by the target three-dimensional printing equipment;
According to the equipment parameters, determining a historical data request record of the target three-dimensional printing equipment from a preset historical database;
determining equipment consistency parameters and equipment risk parameters corresponding to the target three-dimensional printing equipment according to the historical data processing record and the historical data request record;
and determining whether to authorize the target three-dimensional printing equipment according to the equipment consistency parameter and the equipment risk parameter.
As an optional implementation manner, in the first aspect of the present invention, the device parameter includes at least one of a device model number, a device user parameter, a device performance, and a device name.
In a first aspect of the present invention, the determining, according to the device parameter, a history data request record of the target three-dimensional printing device from a preset history database includes:
for any one candidate historical data request record in a preset historical database, acquiring a request equipment parameter corresponding to the historical data request record;
calculating the parameter similarity between the request equipment parameter and the equipment parameter;
and determining all candidate historical data request records with the parameter similarity larger than a preset parameter similarity threshold as the historical data request records of the target three-dimensional printing equipment.
In a first aspect of the present invention, the determining, according to the history data processing record and the history data request record, the device consistency parameter and the device risk parameter corresponding to the target three-dimensional printing device includes:
for any processing record in the historical data processing records and any request record in the historical data request records, calculating the time similarity between the recording time of the processing record and the recording time of the request record according to a preset time similarity algorithm;
judging whether the time similarity is larger than a preset time similarity threshold, if so, classifying the processing record and the request record into a record group;
and determining the equipment consistency parameter and the equipment risk parameter corresponding to the target three-dimensional printing equipment according to the data in each record group and a neural network algorithm.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the data in each record group and a neural network algorithm, a device consistency parameter and a device risk parameter corresponding to the target three-dimensional printing device includes:
For any record group, inputting all the record time and record data corresponding to the processing record and the request record in the record group into a trained risk prediction neural network to obtain risk prediction parameters corresponding to the record group; the risk prediction neural network is obtained through training a training data set comprising a plurality of training record times, training record data and corresponding risk labels;
calculating weighted sum average values of risk prediction parameters of all the record groups to obtain equipment risk parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the number of records in the record group and inversely proportional to the average time interval of all records in the record group;
for any record group, taking all record data of processing records in the record group as a processing record data group, taking all record data of request records in the record group as a request record data group, and inputting the processing record data group and the request record data group into a trained consistency prediction neural network to obtain a consistency prediction parameter corresponding to the record group; the consistency prediction neural network is obtained through training a training data set comprising a plurality of training processing record data sets, a training request record data set and corresponding consistency labels;
Calculating weighted summation average values of the consistency prediction parameters of all the record groups to obtain equipment consistency parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the average of all of the temporal similarities corresponding between all of the records in the record groups.
As an optional implementation manner, in a first aspect of the present invention, the determining whether to authorize the target three-dimensional printing device according to the device consistency parameter and the device risk parameter includes:
judging whether the equipment risk parameter is larger than a preset risk threshold value or not to obtain a first judgment result;
if the first judgment result is yes, determining that the target three-dimensional printing equipment is not authorized;
if the first judgment result is negative, judging whether the equipment consistency parameter is larger than a preset consistency threshold value, and obtaining a second judgment result;
if the second judgment result is yes, determining to authorize the target three-dimensional printing equipment;
and if the second judgment result is negative, determining whether to authorize the target three-dimensional printing equipment or not based on a neural network algorithm according to the equipment consistency parameter and the equipment risk parameter.
As an optional implementation manner, in the first aspect of the present invention, the determining whether to authorize the target three-dimensional printing device based on a neural network algorithm according to the device consistency parameter and the device risk parameter includes:
calculating a difference value between the equipment consistency parameter and the consistency threshold value to obtain a first difference value;
calculating a difference value between the equipment risk parameter and the risk threshold value to obtain a second difference value;
calculating a ratio of the first difference to the second difference;
inputting the ratio to a trained authorization judgment neural network model to obtain an output judgment result, and determining whether to authorize the target three-dimensional printing equipment according to the judgment result; the authorization judgment neural network model is obtained through training a training data set comprising the ratio corresponding to a plurality of data records and whether corresponding authorization labels are obtained.
The second aspect of the present invention discloses a model copyright management system based on a device history record, the system comprising:
the acquisition module is used for responding to an authorization request of the target three-dimensional printing equipment for the target model and acquiring equipment parameters and historical data processing records sent by the target three-dimensional printing equipment;
The screening module is used for determining a historical data request record of the target three-dimensional printing equipment from a preset historical database according to the equipment parameters;
the determining module is used for determining equipment consistency parameters and equipment risk parameters corresponding to the target three-dimensional printing equipment according to the historical data processing record and the historical data request record;
and the authorization module is used for determining whether to authorize the target three-dimensional printing equipment according to the equipment consistency parameter and the equipment risk parameter.
As an optional implementation manner, in the second aspect of the present invention, the device parameter includes at least one of a device model number, a device user parameter, a device performance, and a device name.
In a second aspect of the present invention, the screening module determines, according to the device parameter, a specific manner of the history data request record of the target three-dimensional printing device from a preset history database, where the specific manner includes:
for any one candidate historical data request record in a preset historical database, acquiring a request equipment parameter corresponding to the historical data request record;
Calculating the parameter similarity between the request equipment parameter and the equipment parameter;
and determining all candidate historical data request records with the parameter similarity larger than a preset parameter similarity threshold as the historical data request records of the target three-dimensional printing equipment.
In a second aspect of the present invention, as an optional implementation manner, the determining module determines, according to the history data processing record and the history data request record, a device consistency parameter and a device risk parameter corresponding to the target three-dimensional printing device, where the specific manner includes:
for any processing record in the historical data processing records and any request record in the historical data request records, calculating the time similarity between the recording time of the processing record and the recording time of the request record according to a preset time similarity algorithm;
judging whether the time similarity is larger than a preset time similarity threshold, if so, classifying the processing record and the request record into a record group;
and determining the equipment consistency parameter and the equipment risk parameter corresponding to the target three-dimensional printing equipment according to the data in each record group and a neural network algorithm.
In a second aspect of the present invention, the determining module determines, according to the data in each record group and a neural network algorithm, a specific mode of the device consistency parameter and the device risk parameter corresponding to the target three-dimensional printing device, where the specific mode includes:
for any record group, inputting all the record time and record data corresponding to the processing record and the request record in the record group into a trained risk prediction neural network to obtain risk prediction parameters corresponding to the record group; the risk prediction neural network is obtained through training a training data set comprising a plurality of training record times, training record data and corresponding risk labels;
calculating weighted sum average values of risk prediction parameters of all the record groups to obtain equipment risk parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the number of records in the record group and inversely proportional to the average time interval of all records in the record group;
for any record group, taking all record data of processing records in the record group as a processing record data group, taking all record data of request records in the record group as a request record data group, and inputting the processing record data group and the request record data group into a trained consistency prediction neural network to obtain a consistency prediction parameter corresponding to the record group; the consistency prediction neural network is obtained through training a training data set comprising a plurality of training processing record data sets, a training request record data set and corresponding consistency labels;
Calculating weighted summation average values of the consistency prediction parameters of all the record groups to obtain equipment consistency parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the average of all of the temporal similarities corresponding between all of the records in the record groups.
As an optional implementation manner, in the second aspect of the present invention, the determining, by the authorization module, whether to authorize the target three-dimensional printing device according to the device consistency parameter and the device risk parameter includes:
judging whether the equipment risk parameter is larger than a preset risk threshold value or not to obtain a first judgment result;
if the first judgment result is yes, determining that the target three-dimensional printing equipment is not authorized;
if the first judgment result is negative, judging whether the equipment consistency parameter is larger than a preset consistency threshold value, and obtaining a second judgment result;
if the second judgment result is yes, determining to authorize the target three-dimensional printing equipment;
and if the second judgment result is negative, determining whether to authorize the target three-dimensional printing equipment or not based on a neural network algorithm according to the equipment consistency parameter and the equipment risk parameter.
As an optional implementation manner, in the second aspect of the present invention, the determining, by the authorization module, whether to authorize the target three-dimensional printing device based on a neural network algorithm according to the device consistency parameter and the device risk parameter includes:
calculating a difference value between the equipment consistency parameter and the consistency threshold value to obtain a first difference value;
calculating a difference value between the equipment risk parameter and the risk threshold value to obtain a second difference value;
calculating a ratio of the first difference to the second difference;
inputting the ratio to a trained authorization judgment neural network model to obtain an output judgment result, and determining whether to authorize the target three-dimensional printing equipment according to the judgment result; the authorization judgment neural network model is obtained through training a training data set comprising the ratio corresponding to a plurality of data records and whether corresponding authorization labels are obtained.
The third aspect of the present invention discloses another model copyright management system based on device history, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform some or all of the steps in the device history-based model rights management method disclosed in the first aspect of the invention.
A fourth aspect of the present invention discloses a computer storage medium storing computer instructions for performing part or all of the steps in the device history-based model rights management method disclosed in the first aspect of the present invention when called.
Compared with the prior art, the invention has the following beneficial effects:
the invention can more comprehensively analyze the consistency and the danger of the equipment based on the history request record and the data processing record of the equipment so as to determine whether to authorize, thereby achieving safer and finer model copyright management and reducing the possibility of the model being stolen by attack.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a method for managing model copyrights based on device history according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a device history-based model rights management system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another model rights management system based on device history according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a model copyright management method and a system based on a device history record, which can more comprehensively analyze the consistency and the risk of the device based on the history request record and the data processing record of the device so as to determine whether to authorize or not, thereby achieving safer and finer model copyright management and reducing the possibility of the model being stolen by attack. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a model copyright management method based on a device history record according to an embodiment of the present invention. The method described in fig. 1 may be applied to a corresponding data processing device, a data processing terminal, and a data processing server, where the server may be a local server or a cloud server, and the embodiment of the present invention is not limited to the method shown in fig. 1, and the method for managing model rights based on device history may include the following operations:
101. And responding to the authorization request of the target three-dimensional printing equipment for the target model, and acquiring equipment parameters and historical data processing records sent by the target three-dimensional printing equipment.
Alternatively, the history data processing record may be details of data processed during a history period recorded and saved by the data processor of the target three-dimensional printing device, and transmitted by the target three-dimensional printing device.
102. And determining a historical data request record of the target three-dimensional printing equipment from a preset historical database according to the equipment parameters.
Optionally, the device parameter includes at least one of a device model number, a device user parameter, a device performance, a device name.
103. And determining equipment consistency parameters and equipment risk parameters corresponding to the target three-dimensional printing equipment according to the historical data processing record and the historical data request record.
104. And determining whether to authorize the target three-dimensional printing device according to the device consistency parameter and the device risk parameter.
Therefore, the method described by the embodiment of the invention can more comprehensively analyze the consistency and the risk of the equipment based on the history request record and the data processing record of the equipment so as to determine whether the equipment is authorized, thereby achieving safer and more detailed model copyright management and reducing the possibility of the model being stolen by attack.
As an optional embodiment, in the step, according to the device parameter, determining the historical data request record of the target three-dimensional printing device from a preset historical database includes:
for any one candidate historical data request record in a preset historical database, acquiring a request equipment parameter corresponding to the historical data request record;
calculating the parameter similarity between the request equipment parameter and the equipment parameter;
and determining all candidate historical data request records with parameter similarity larger than a preset parameter similarity threshold as the historical data request records of the target three-dimensional printing equipment.
Alternatively, the parameter similarity may be calculated by a data similarity algorithm, such as a vector distance algorithm or other algorithm for data consistency measurement.
Through the embodiment, all candidate historical data request records with parameter similarity larger than the preset parameter similarity threshold can be screened out and determined to be the historical data request record of the target three-dimensional printing equipment, so that the consistency and the dangerousness of the equipment can be more comprehensively analyzed according to the historical data request record later, safer and finer model copyright management is achieved, and the possibility that the model is stolen by attack is reduced.
As an optional embodiment, in the step, determining, according to the history data processing record and the history data request record, a device consistency parameter and a device risk parameter corresponding to the target three-dimensional printing device includes:
for any processing record in the historical data processing records and any request record in the historical data request records, calculating the time similarity between the recording time of the processing record and the recording time of the request record according to a preset time similarity algorithm;
judging whether the time similarity is larger than a preset time similarity threshold, if so, classifying the processing record and the request record into a record group;
and determining the equipment consistency parameter and the equipment risk parameter corresponding to the target three-dimensional printing equipment according to the data in each record group and the neural network algorithm.
Optionally, the time similarity algorithm may determine whether the time difference between two recording times falls within a preset reasonable adjacent time difference interval according to the preset reasonable adjacent time difference interval, so as to determine the similarity, or predict the similarity according to a trained neural network model.
Through the embodiment, the records with high time similarity can be combined to obtain a plurality of record groups, and the consistency and the dangerousness of the equipment can be more comprehensively analyzed according to the associated data in the record groups, so that safer and finer model copyright management is achieved, and the possibility of the model being stolen by attack is reduced.
As an optional embodiment, in the step, determining, according to the data in each record group and the neural network algorithm, a device consistency parameter and a device risk parameter corresponding to the target three-dimensional printing device includes:
for any record group, inputting all the record time and record data corresponding to the processing record and the request record in the record group into a trained risk prediction neural network to obtain risk prediction parameters corresponding to the record group; the risk prediction neural network is obtained through training a training data set comprising a plurality of training record times, training record data and corresponding risk labels;
calculating weighted summation average values of risk prediction parameters of all record groups to obtain equipment risk parameters corresponding to target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each record group is proportional to the number of records in the record group and inversely proportional to the average time interval of all records in the record group;
for any record group, taking all record data of the processing records in the record group as a processing record data group, taking all record data of the request records in the record group as a request record data group, and inputting the processing record data group and the request record data group into a trained consistency prediction neural network to obtain a consistency prediction parameter corresponding to the record group; the consistency prediction neural network is obtained by training a training data set comprising a plurality of training processing record data sets, a training request record data set and corresponding consistency labels;
Calculating weighted summation average values of consistency prediction parameters of all record groups to obtain equipment consistency parameters corresponding to target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each record group is proportional to the average of all temporal similarities corresponding between all records in the record group.
Alternatively, the neural network model in the present invention may be a neural network model of a CNN structure, an RNN structure, or an LTSM structure.
Through the embodiment, the consistency and the risk of the data in the record groups can be predicted according to the data in each record group, the neural network algorithm and the weight algorithm, so that the equipment consistency parameter and the equipment risk parameter corresponding to the target three-dimensional printing equipment can be determined, whether authorization is realized or not can be determined according to the two parameters, safer and finer model copyright management is achieved, and the possibility of the model being stolen by attack is reduced.
As an optional embodiment, in the step, determining whether to authorize the target three-dimensional printing device according to the device consistency parameter and the device risk parameter includes:
judging whether the equipment risk parameter is larger than a preset risk threshold value or not to obtain a first judgment result;
If the first judgment result is yes, determining that the target three-dimensional printing equipment is not authorized;
if the first judgment result is negative, judging whether the equipment consistency parameter is larger than a preset consistency threshold value, and obtaining a second judgment result;
if the second judgment result is yes, determining to authorize the target three-dimensional printing equipment;
if the second judgment result is negative, determining whether to authorize the target three-dimensional printing equipment or not based on a neural network algorithm according to the equipment consistency parameter and the equipment risk parameter.
Through the embodiment, whether the authorization is determined according to the equipment consistency parameter, the equipment risk parameter and the preset judging rule, so that the authorization is directly refused when the risk is high, and the authorization is directly authorized when the risk is low and the consistency is high, thereby achieving safer and finer model copyright management and reducing the possibility of the model being stolen by attack.
As an optional embodiment, in the step, determining whether to authorize the target three-dimensional printing device based on the neural network algorithm according to the device consistency parameter and the device risk parameter includes:
calculating a difference value between the consistency parameter of the equipment and a consistency threshold value to obtain a first difference value;
Calculating a difference value between the equipment risk parameter and a risk threshold value to obtain a second difference value;
calculating the ratio of the first difference value to the second difference value;
inputting the ratio to a trained authorization judgment neural network model to obtain an output judgment result, and determining whether to authorize the target three-dimensional printing equipment according to the judgment result; the authorization judgment neural network model is obtained through training a training data set comprising corresponding ratio values of a plurality of data records and corresponding authorization labels.
Meanwhile, the ratio can be used for measuring the prediction accuracy of the neural network model, because the consistency of equipment and the risk of the equipment are obviously in an anti-correlation relationship, and the risk of the inconsistent equipment is accurately predicted under the condition of impersonation attack, so that the ratio can show a certain data characteristic under the condition of inaccurate model judgment, and the prediction and the identification can be carried out through the neural network model.
Through the embodiment, whether the target three-dimensional printing equipment is authorized or not can be determined according to the difference ratio of the consistency parameter and the risk parameter and the judgment result of the authorization judgment neural network model, so that safer and finer model copyright management is achieved, and the possibility of the model being stolen by attack is reduced.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a model rights management system based on a device history record according to an embodiment of the present invention. The system described in fig. 2 may be applied to a corresponding data processing device, a data processing terminal, and a data processing server, where the server may be a local server or a cloud server, and embodiments of the present invention are not limited. As shown in fig. 2, the system may include:
an obtaining module 201, configured to obtain, in response to an authorization request of a target three-dimensional printing device for a target model, a device parameter and a historical data processing record sent by the target three-dimensional printing device;
the screening module 202 is configured to determine, according to the device parameter, a history data request record of the target three-dimensional printing device from a preset history database;
a determining module 203, configured to determine, according to the history data processing record and the history data request record, a device consistency parameter and a device risk parameter corresponding to the target three-dimensional printing device;
and the authorization module 204 is used for determining whether to authorize the target three-dimensional printing device according to the device consistency parameter and the device risk parameter.
As an alternative embodiment, the device parameters include at least one of a device model number, a device user parameter, a device performance, a device name.
As an optional embodiment, the screening module 202 determines, according to the device parameter, a specific manner of the history data request record of the target three-dimensional printing device from a preset history database, where the specific manner includes:
for any one candidate historical data request record in a preset historical database, acquiring a request equipment parameter corresponding to the historical data request record;
calculating the parameter similarity between the request equipment parameter and the equipment parameter;
and determining all candidate historical data request records with parameter similarity larger than a preset parameter similarity threshold as the historical data request records of the target three-dimensional printing equipment.
As an optional embodiment, the determining module 203 determines, according to the history data processing record and the history data request record, a specific manner of determining the device consistency parameter and the device risk parameter corresponding to the target three-dimensional printing device, where the specific manner includes:
for any processing record in the historical data processing records and any request record in the historical data request records, calculating the time similarity between the recording time of the processing record and the recording time of the request record according to a preset time similarity algorithm;
Judging whether the time similarity is larger than a preset time similarity threshold, if so, classifying the processing record and the request record into a record group;
and determining the equipment consistency parameter and the equipment risk parameter corresponding to the target three-dimensional printing equipment according to the data in each record group and the neural network algorithm.
As an optional embodiment, the determining module 203 determines, according to the data in each record group and the neural network algorithm, a specific manner of determining the device consistency parameter and the device risk parameter corresponding to the target three-dimensional printing device, where the specific manner includes:
for any record group, inputting all the record time and record data corresponding to the processing record and the request record in the record group into a trained risk prediction neural network to obtain risk prediction parameters corresponding to the record group; the risk prediction neural network is obtained through training a training data set comprising a plurality of training record times, training record data and corresponding risk labels;
calculating weighted summation average values of risk prediction parameters of all record groups to obtain equipment risk parameters corresponding to target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each record group is proportional to the number of records in the record group and inversely proportional to the average time interval of all records in the record group;
For any record group, taking all record data of the processing records in the record group as a processing record data group, taking all record data of the request records in the record group as a request record data group, and inputting the processing record data group and the request record data group into a trained consistency prediction neural network to obtain a consistency prediction parameter corresponding to the record group; the consistency prediction neural network is obtained by training a training data set comprising a plurality of training processing record data sets, a training request record data set and corresponding consistency labels;
calculating weighted summation average values of consistency prediction parameters of all record groups to obtain equipment consistency parameters corresponding to target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each record group is proportional to the average of all temporal similarities corresponding between all records in the record group.
As an alternative embodiment, the authorization module 204 determines, according to the device consistency parameter and the device risk parameter, a specific manner of whether to authorize the target three-dimensional printing device, including:
judging whether the equipment risk parameter is larger than a preset risk threshold value or not to obtain a first judgment result;
If the first judgment result is yes, determining that the target three-dimensional printing equipment is not authorized;
if the first judgment result is negative, judging whether the equipment consistency parameter is larger than a preset consistency threshold value, and obtaining a second judgment result;
if the second judgment result is yes, determining to authorize the target three-dimensional printing equipment;
if the second judgment result is negative, determining whether to authorize the target three-dimensional printing equipment or not based on a neural network algorithm according to the equipment consistency parameter and the equipment risk parameter.
As an alternative embodiment, the authorization module 204 determines, based on a neural network algorithm, a specific manner of whether to authorize the target three-dimensional printing device according to the device consistency parameter and the device risk parameter, including:
calculating a difference value between the consistency parameter of the equipment and a consistency threshold value to obtain a first difference value;
calculating a difference value between the equipment risk parameter and a risk threshold value to obtain a second difference value;
calculating the ratio of the first difference value to the second difference value;
inputting the ratio to a trained authorization judgment neural network model to obtain an output judgment result, and determining whether to authorize the target three-dimensional printing equipment according to the judgment result; the authorization judgment neural network model is obtained through training a training data set comprising corresponding ratio values of a plurality of data records and corresponding authorization labels.
The details and technical effects of the modules in the embodiment of the present invention may refer to the description in the first embodiment, and are not described herein.
Example III
Referring to fig. 3, fig. 3 is a schematic structural diagram of another model rights management system based on device history according to an embodiment of the present invention. As shown in fig. 3, the system may include:
a memory 301 storing executable program code;
a processor 302 coupled with the memory 301;
the processor 302 invokes executable program code stored in the memory 301 to perform some or all of the steps in the device history-based model rights management method disclosed in the embodiment of the present invention.
Example IV
The embodiment of the invention discloses a computer storage medium which stores computer instructions for executing part or all of the steps in the model copyright management method based on the equipment history record disclosed in the embodiment of the invention when the computer instructions are called.
The system embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a model copyright management method and system based on equipment history record, which are disclosed by the embodiment of the invention only for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (8)
1. A method for model rights management based on device history, the method comprising:
responding to an authorization request of target three-dimensional printing equipment for a target model, and acquiring equipment parameters and historical data processing records sent by the target three-dimensional printing equipment;
according to the equipment parameters, determining a historical data request record of the target three-dimensional printing equipment from a preset historical database;
for any processing record in the historical data processing records and any request record in the historical data request records, calculating the time similarity between the recording time of the processing record and the recording time of the request record according to a preset time similarity algorithm;
Judging whether the time similarity is larger than a preset time similarity threshold, if so, classifying the processing record and the request record into a record group;
for any record group, inputting all the record time and record data corresponding to the processing record and the request record in the record group into a trained risk prediction neural network to obtain risk prediction parameters corresponding to the record group; the risk prediction neural network is obtained through training a training data set comprising a plurality of training record times, training record data and corresponding risk labels;
calculating weighted sum average values of risk prediction parameters of all the record groups to obtain equipment risk parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the number of records in the record group and inversely proportional to the average time interval of all records in the record group;
for any record group, taking all record data of processing records in the record group as a processing record data group, taking all record data of request records in the record group as a request record data group, and inputting the processing record data group and the request record data group into a trained consistency prediction neural network to obtain a consistency prediction parameter corresponding to the record group; the consistency prediction neural network is obtained through training a training data set comprising a plurality of training processing record data sets, a training request record data set and corresponding consistency labels;
Calculating weighted summation average values of the consistency prediction parameters of all the record groups to obtain equipment consistency parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the average of all of the temporal similarities corresponding between all of the records in the record group;
and determining whether to authorize the target three-dimensional printing equipment according to the equipment consistency parameter and the equipment risk parameter.
2. The device history based model rights management method of claim 1, wherein the device parameters include at least one of a device model number, a device user parameter, a device performance, a device name.
3. The device history record-based model copyright management method according to claim 2, wherein the determining the history data request record of the target three-dimensional printing device from a preset history database according to the device parameters comprises:
for any one candidate historical data request record in a preset historical database, acquiring a request equipment parameter corresponding to the historical data request record;
Calculating the parameter similarity between the request equipment parameter and the equipment parameter;
and determining all candidate historical data request records with the parameter similarity larger than a preset parameter similarity threshold as the historical data request records of the target three-dimensional printing equipment.
4. The device history based model rights management method of claim 1, wherein the determining whether to authorize the target three-dimensional printing device according to the device consistency parameter and the device risk parameter comprises:
judging whether the equipment risk parameter is larger than a preset risk threshold value or not to obtain a first judgment result;
if the first judgment result is yes, determining that the target three-dimensional printing equipment is not authorized;
if the first judgment result is negative, judging whether the equipment consistency parameter is larger than a preset consistency threshold value, and obtaining a second judgment result;
if the second judgment result is yes, determining to authorize the target three-dimensional printing equipment;
and if the second judgment result is negative, determining whether to authorize the target three-dimensional printing equipment or not based on a neural network algorithm according to the equipment consistency parameter and the equipment risk parameter.
5. The device history based model rights management method of claim 4, wherein said determining whether to authorize the target three-dimensional printing device based on a neural network algorithm based on the device consistency parameter and the device risk parameter comprises:
calculating a difference value between the equipment consistency parameter and the consistency threshold value to obtain a first difference value;
calculating a difference value between the equipment risk parameter and the risk threshold value to obtain a second difference value;
calculating a ratio of the first difference to the second difference;
inputting the ratio to a trained authorization judgment neural network model to obtain an output judgment result, and determining whether to authorize the target three-dimensional printing equipment according to the judgment result; the authorization judgment neural network model is obtained through training a training data set comprising the ratio corresponding to a plurality of data records and whether corresponding authorization labels are obtained.
6. A model rights management system based on a device history record, the system comprising:
the acquisition module is used for responding to an authorization request of the target three-dimensional printing equipment for the target model and acquiring equipment parameters and historical data processing records sent by the target three-dimensional printing equipment;
The screening module is used for determining a historical data request record of the target three-dimensional printing equipment from a preset historical database according to the equipment parameters;
the determining module is configured to determine, according to the history data processing record and the history data request record, an equipment consistency parameter and an equipment risk parameter corresponding to the target three-dimensional printing equipment, and specifically includes:
for any processing record in the historical data processing records and any request record in the historical data request records, calculating the time similarity between the recording time of the processing record and the recording time of the request record according to a preset time similarity algorithm;
judging whether the time similarity is larger than a preset time similarity threshold, if so, classifying the processing record and the request record into a record group;
for any record group, inputting all the record time and record data corresponding to the processing record and the request record in the record group into a trained risk prediction neural network to obtain risk prediction parameters corresponding to the record group; the risk prediction neural network is obtained through training a training data set comprising a plurality of training record times, training record data and corresponding risk labels;
Calculating weighted sum average values of risk prediction parameters of all the record groups to obtain equipment risk parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the number of records in the record group and inversely proportional to the average time interval of all records in the record group;
for any record group, taking all record data of processing records in the record group as a processing record data group, taking all record data of request records in the record group as a request record data group, and inputting the processing record data group and the request record data group into a trained consistency prediction neural network to obtain a consistency prediction parameter corresponding to the record group; the consistency prediction neural network is obtained through training a training data set comprising a plurality of training processing record data sets, a training request record data set and corresponding consistency labels;
calculating weighted summation average values of the consistency prediction parameters of all the record groups to obtain equipment consistency parameters corresponding to the target three-dimensional printing equipment; wherein the weight of the risk prediction parameter for each of the record groups is proportional to the average of all of the temporal similarities corresponding between all of the records in the record group;
And the authorization module is used for determining whether to authorize the target three-dimensional printing equipment according to the equipment consistency parameter and the equipment risk parameter.
7. A model rights management system based on a device history record, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the device history based model rights management method of any one of claims 1-5.
8. A computer storage medium storing computer instructions which, when invoked, are operable to perform the device history based model rights management method of any one of claims 1-5.
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| CN117390684B (en) * | 2023-12-06 | 2024-04-09 | 深圳市智能派科技有限公司 | Data encryption processing method and system based on slice level association |
| CN118037046B (en) * | 2024-02-21 | 2024-06-21 | 广州番禺职业技术学院 | Asset data processing method and system based on history record |
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