CN115114666A - Attendance data privacy calculation method and system based on block chain - Google Patents
Attendance data privacy calculation method and system based on block chain Download PDFInfo
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- CN115114666A CN115114666A CN202211027903.XA CN202211027903A CN115114666A CN 115114666 A CN115114666 A CN 115114666A CN 202211027903 A CN202211027903 A CN 202211027903A CN 115114666 A CN115114666 A CN 115114666A
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/10—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people together with the recording, indicating or registering of other data, e.g. of signs of identity
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Abstract
The invention discloses a block chain-based attendance data privacy calculation method and system, and relates to the technical field of block chains. One embodiment of the method comprises: the RPA service module acquires attendance checking parameters, checks the attendance checking parameters according to parameter rules, acquires attendance checking data according to the attendance checking parameters if the checking is passed, and sends the attendance checking data to an attendance checking statistical system; the attendance statistical system generates a target execution code corresponding to the target attendance rule according to the code fragment corresponding to the attendance rule, performs statistical calculation on attendance data according to the target execution code to obtain a statistical result, and uploads the statistical result and the attendance data to a block chain; the privacy computing system acquires statistical results and/or attendance data from the blockchain; and carrying out privacy calculation on the basis of the statistical result and/or attendance data and data acquired from an external data source to obtain a privacy calculation result. According to the implementation mode, the attendance data and the external data are combined, and the application of the attendance data is widened.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a block chain-based attendance data privacy calculation method and system.
Background
Attendance data are generally manually exported by attendance checking and accounting personnel, are stored in an electronic document or spreadsheet mode, and are subjected to statistical calculation according to the attendance and performance rules of a company. Therefore, at present, the use of attendance data is limited to obtaining statistical results, and how to deeply mine the value of the attendance data, so that the use range of the attendance data is widened, and the problem of attention of technical personnel is solved.
Disclosure of Invention
In view of this, embodiments of the present invention provide a block chain-based attendance data privacy calculation method and system, which can combine attendance data with external data to further mine the value of the attendance data.
In a first aspect, an embodiment of the present invention provides a block chain-based attendance data privacy calculation method, including:
an RPA (robot Process Automation) service module acquires attendance parameters, checks the attendance parameters according to a preset parameter rule, acquires attendance data according to the attendance parameters if the checking is passed, and sends the attendance data to an attendance statistical system;
the attendance statistical system generates a target execution code corresponding to a target attendance rule according to a preset code fragment corresponding to an attendance rule, performs statistical calculation on the attendance data according to the target execution code to obtain a statistical result, and uploads the statistical result and the attendance data to a block chain; the target attendance rules consist of a plurality of attendance sub rules;
the privacy computing system acquires the statistical result and/or the attendance data from the blockchain; and carrying out privacy calculation based on the statistical result and/or the attendance data and data acquired from an external data source to obtain a privacy calculation result.
In a second aspect, an embodiment of the present invention provides a block chain-based attendance data privacy computing system, including: the system comprises an RPA service module, an attendance statistic system and a privacy computing system;
the RPA service module is used for acquiring attendance parameters, verifying the attendance parameters according to preset parameter rules, acquiring attendance data according to the attendance parameters if verification is passed, and sending the attendance data to the attendance statistical system;
the attendance statistical system is used for generating a target execution code corresponding to a target attendance rule according to a preset code fragment corresponding to an attendance rule, performing statistical calculation on the attendance data according to the target execution code to obtain a statistical result, and uploading the statistical result and the attendance data to a block chain; the target attendance rules consist of a plurality of attendance sub rules;
the privacy computing system is used for acquiring the statistical result and/or the attendance data from the block chain; and carrying out privacy calculation based on the statistical result and/or the attendance data and data acquired from an external data source to obtain a privacy calculation result.
One embodiment of the above invention has the following advantages or benefits: the attendance checking method and the attendance checking system are used for checking the attendance checking parameters based on the preset parameter rules, and can filter out the non-standard or illegal statistical demands. The execution code of the attendance rule is dynamically generated through the preset code segment, when the attendance rule changes, the execution code after the attendance rule is updated can be quickly obtained through adjusting the code segment in the execution code, and compared with the method for rewriting the execution code, the method and the device for checking the attendance rule can reduce workload and improve statistical efficiency. The attendance data and the statistical result are uploaded to the block chain for storage, and the storage safety of the attendance data and the statistical result can be improved. In addition, the attendance data and/or the statistical result and the external data are combined to carry out privacy calculation, the privacy calculation result can be applied to scenes such as travel early warning and staff training, the use range of the attendance data is expanded, and the value of the attendance data is further exerted.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a flowchart of a block chain-based attendance data privacy calculation method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a block chain-based attendance data privacy computing system according to an embodiment of the present invention;
fig. 3 is a flowchart of a block chain-based attendance data privacy calculation method according to another embodiment of the present invention;
fig. 4 is a schematic diagram of an RPA service module according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an attendance rule subsystem according to an embodiment of the present invention;
FIG. 6 is a block chain core subsystem according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a privacy computing system provided by one embodiment of the present invention;
FIG. 8 is a schematic diagram of a privacy computation module provided by an embodiment of the invention;
fig. 9 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, an embodiment of the present invention provides a block chain-based attendance data privacy calculation method, including:
step 101: and the RPA service module acquires attendance checking parameters.
The attendance parameters can be employee names, job numbers, dates, departments and the like, namely, the user can count attendance data from dimensions of the employee names, the job numbers, the dates, the departments and the like.
Step 102: checking the attendance parameters according to a preset parameter rule, and acquiring attendance data according to the attendance parameters when the checking is passed.
In consideration of the fact that the attendance parameters provided by the user may have errors, the attendance parameters are verified through the preset parameter rules. For example, a user wants to count attendance data of three-inch and one-month employees, whether three-inch is the employee of the unit can be checked according to parameter rules, if yes, the checking is passed, the attendance data of three-inch and one-month is acquired from attendance equipment or attendance software according to attendance parameters, and if not, the checking fails.
Step 103: and sending the attendance data to an attendance statistical system.
Step 104: the attendance statistical system generates a target execution code corresponding to the target attendance rule according to a preset code segment corresponding to the attendance rule.
Step 105: and performing statistical calculation on the attendance data according to the target execution code to obtain a statistical result.
The target attendance rules are composed of a plurality of attendance rules.
Step 106: and uploading the statistical result and the attendance data to a block chain.
The attendance statistical system can determine an execution code for processing attendance data according to the triggering operation of the user. For example, the attendance statistic system can display the attendance rule and the code segments corresponding to the attendance rule, the user can select a plurality of code segments according to the attendance rule, the code segments form the execution code, and the attendance rule of the code segments forms the attendance rule corresponding to the execution code. And uploading the statistical result and the attendance data to a block chain for storage.
Step 107: and the privacy computing system acquires the statistical result and attendance data from the block chain.
Step 108: and carrying out privacy calculation based on the statistical result, the attendance data and the data acquired from the external data source to obtain a privacy calculation result.
In a practical application scenario, the privacy calculation may be performed based on the attendance data and data acquired from an external data source, the privacy calculation may be performed based on the statistics and data acquired from an external data source, and the privacy calculation may be performed based on the attendance data, the statistics, and data acquired from an external data source. Data acquired by an external data source can be data in different scenes such as weather data, road data and shopping data, and privacy calculation results can be used for recommending commuting lines for employees, recommending nearby convenience stores for employees and the like.
The attendance checking method and the attendance checking system are used for checking the attendance checking parameters based on the preset parameter rules, and can filter out the non-standard or illegal statistical demands. The execution code of the attendance rule is dynamically generated through the preset code segment, when the attendance rule changes, the execution code after the attendance rule is updated can be quickly acquired through adjusting the code segment in the execution code, and compared with the method of rewriting the execution code, the method and the device for checking attendance can reduce workload and improve statistical efficiency. The attendance data and the statistical result are uploaded to the block chain for storage, and the storage safety of the attendance data and the statistical result can be improved. In addition, the embodiment of the invention combines the attendance data and external data to perform privacy calculation, and the privacy calculation result can be applied to scenes such as trip early warning, staff training and the like, so that the application range of the attendance data is expanded, and the value of the attendance data is further exerted.
In one embodiment of the invention, the method further comprises:
and if the verification fails, the RPA service module generates an error message according to the attendance checking parameters and sends out an alarm signal according to the error message.
If the verification is not passed, the attendance parameters provided by the user do not meet the attendance rules and need to be modified. According to the embodiment of the invention, the alarm information is sent out to prompt the user and the manager of the attendance data privacy calculation system, for example, the attendance statistical system displays the word of 'the attendance parameter is input with errors' on the screen.
In an embodiment of the present invention, acquiring attendance data according to the attendance parameters includes:
and acquiring attendance data from the attendance equipment according to the attendance parameters according to a preset period.
According to the embodiment of the invention, the attendance data can be automatically acquired from the attendance equipment or the attendance software according to the preset period, manual derivation by a user is not required, and the acquisition efficiency of the attendance data can be improved.
In one embodiment of the invention, the acquiring of attendance data according to the attendance parameters comprises:
and monitoring whether a preset trigger event occurs, and if so, acquiring attendance data from the attendance equipment according to the attendance parameters.
The trigger event may be employee vacation, employee early-quit, user click on a preset button, and the like, for example, when three vacations are monitored, attendance data such as time length of the three vacations and examination approval of the three vacations are acquired from the attendance equipment according to attendance parameters. According to the embodiment of the invention, the attendance data can be automatically acquired after the triggering event occurs, and the data does not need to be manually exported from the attendance equipment by a user.
In an embodiment of the invention, the generating, by the attendance statistics system, a target execution code corresponding to the target attendance rule according to a preset code segment corresponding to the attendance rule includes:
the attendance statistical system displays a plurality of attendance sub rules and code fragments of the attendance sub rules, selects a plurality of target attendance sub rules from the plurality of attendance sub rules according to the triggering operation of a user, and generates target execution codes corresponding to the target attendance sub rules according to the code fragments of the plurality of target attendance sub rules;
the target attendance rules are composed of a plurality of target attendance sub rules.
A plurality of attendance sub rules and corresponding code segments can be preset in the attendance statistical system, the attendance sub rules and the code segments can be displayed to a user, the user can select the code segments according to the attendance sub rules according to the self requirements, and the selected target code segments form a target execution code. The user can add, delete and change code segments. When the attendance checking rule is changed, the user does not need to rewrite the execution code, but only needs to change the code segments forming the execution code. Therefore, the attendance data statistical efficiency can be improved.
In one embodiment of the present invention, uploading the statistics and attendance data into the blockchain includes:
determining the identification of the target intelligent contract and the operation type of the statistical result;
calling a target intelligent contract which is pre-deployed in a block chain according to the identification of the target intelligent contract, packaging a statistical result and attendance data into blocks, and adding a timestamp to the blocks;
sequencing blocks formed by packaging a plurality of statistical results;
and adding a plurality of blocks into the block chain according to the sorting result and the operation type.
In the embodiment of the present invention, different operation types correspond to different intelligent contracts, and the operation types may be determined by specific service scenarios, for example, the operation types include day statistics, month statistics, department statistics, and the like. Different intelligent contracts add blocks to different block chain child chains. By the embodiment of the invention, the attendance data and the statistical result can be stored in the block chain, so that the data is prevented from being tampered, and the storage safety of the data is improved. In addition, different intelligent contracts are called to store the data according to the operation types, so that the requirements of actual services are fully considered, and subsequent data query is facilitated. In an actual application scenario, different operation types may also correspond to the same intelligent contract, and details are not repeated here.
In one embodiment of the invention, the privacy calculation is performed based on the statistical result and/or attendance data and data acquired from an external data source, and comprises the following steps:
and calling a trusted execution environment and/or a graphic processor and/or a field programmable logic gate array to execute privacy calculation based on the statistical result and/or the attendance data and the data acquired from the external data source.
The embodiment of the invention can improve the speed of privacy calculation through a trusted execution environment and/or a graphic processor and/or a field programmable gate array.
In one embodiment of the invention, the privacy calculation is performed based on the statistical result and/or attendance data and data obtained from an external data source, and comprises the following steps:
based on the multi-party security calculation, performing privacy calculation on the statistical result and/or the attendance data and data obtained from an external data source.
Under the condition of no reliable third party, the multi-party safe calculation can encrypt own data before all parties calculate together, and each participant can not know information input by other parties and can only obtain a calculation result. The privacy and the safety of the attendance data can be ensured through the multi-party safety calculation.
In one embodiment of the invention, the privacy calculation is performed based on the statistical result and/or attendance data and data acquired from an external data source, and comprises the following steps:
training a federal learning model based on historical statistical results and/or historical attendance data and historical data acquired from an external data source to obtain a trained federal learning model;
and inputting the current statistical result and/or the current attendance data and the current data acquired from an external data source into the trained federated learning model.
On the basis of ensuring the privacy and the safety of data, the federal study can realize multi-party common modeling and improve the prediction effect of the model. The federal study does not need the participator to transmit data to the central model for operation, but after a small model is trained locally, the trained model and the trained models of other parties are transmitted to the system platform for integrated debugging, so as to achieve the optimization purpose. The method not only realizes that the data is not local, but also achieves the purposes of joint calculation and modeling. In an actual application scene, a model obtained by federal learning training can be provided for a third party for use, so that the application of attendance data is further widened, and the value of the attendance data is improved.
In addition to multi-party security computation and federal learning, privacy computation can be performed using methods such as differential privacy, homomorphic encryption, and the like.
In one embodiment of the invention, the associated data of the attendance checking data is obtained from a database;
the statistical calculation of the attendance data according to the target execution code comprises the following steps:
and performing statistical calculation on the attendance data and the associated data according to the target execution code.
Considering that attendance data acquired from attendance equipment or attendance software is possibly simple and cannot meet statistical requirements, at the moment, associated data of the attendance data can be acquired from the database. For example, the attendance data includes employee numbers and time of card punching, and in order to count the attendance data by department, it is necessary to acquire information of the department to which the employee belongs from the database according to the employee numbers. Therefore, the embodiment of the invention can meet various statistical requirements of users.
As shown in fig. 2, an embodiment of the present invention provides a block chain-based attendance data privacy computing system, including: the system comprises an RPA service module 201, an attendance statistic system 202 and a privacy calculation system 203;
the RPA service module 201 is configured to acquire an attendance parameter, verify the attendance parameter according to a preset parameter rule, acquire attendance data according to the attendance parameter if the verification passes, and send the attendance data to the attendance statistical system 202;
the attendance statistics system 202 is used for generating a target execution code corresponding to the target attendance rule according to a preset code segment corresponding to the attendance rule, performing statistical calculation on attendance data according to the target execution code to obtain a statistical result, and uploading the statistical result and the attendance data to a block chain; the target attendance rules are composed of a plurality of attendance rules;
the privacy computing system 203 is used for acquiring statistical results and/or attendance data from the blockchain; and carrying out privacy calculation based on the statistical result and/or attendance data and data acquired from an external data source to obtain a privacy calculation result.
The RPA service module 201 includes: an RPA scheduler, an RPA designer, and a plurality of execution components. The attendance statistics system 202 comprises an attendance rule subsystem and a block chain core subsystem, wherein the attendance rule subsystem comprises a data interface module, a database management module and an attendance rule module, and the block chain core subsystem comprises a contract execution module, a sequencing uplink module and a block chain query module. The privacy computing system 203 comprises a data adding module, a data applying module, a privacy computing module, a hardware interface module, a result output module and a model exporting module. The subsequent embodiments will further explain the functions of the respective modules.
In an embodiment of the present invention, the RPA service module 201 is configured to, if the check fails, generate an error message according to the attendance parameter, and send an alarm signal according to the error message.
In an embodiment of the present invention, the RPA service module 201 is configured to acquire attendance data from an attendance device according to an attendance parameter according to a preset period.
In an embodiment of the present invention, the RPA service module 201 is configured to monitor whether a preset trigger event occurs, and if so, obtain attendance data from the attendance device according to the attendance parameters.
In an embodiment of the invention, the attendance statistics system 202 is configured to display a plurality of attendance sub-rules and code segments of the attendance sub-rules, select a plurality of target attendance sub-rules from the plurality of attendance sub-rules according to a trigger operation of a user, and generate a target execution code corresponding to the target attendance sub-rules according to the code segments of the plurality of target attendance sub-rules;
the target attendance rules are composed of a plurality of target attendance sub rules.
In an embodiment of the present invention, the attendance statistics system 202 is configured to determine an identification of the target intelligent contract and an operation type of the statistical result; calling a target intelligent contract which is pre-deployed in a block chain according to the identification of the target intelligent contract, packaging a statistical result and attendance data into blocks, and adding a timestamp to the blocks; sequencing blocks formed by packaging a plurality of statistical results; and adding a plurality of blocks into the block chain according to the sorting result and the operation type.
In an embodiment of the present invention, the privacy computing system 203 is configured to invoke a trusted execution environment and/or a graphics processor and/or a field programmable gate array to perform the privacy computation based on the statistics and/or the attendance data and data obtained from an external data source.
In one embodiment of the invention, the privacy computing system 203 is configured to perform privacy computation on the statistics and/or the attendance data and data obtained from an external data source based on the multiparty security computation.
In an embodiment of the invention, the privacy computing system 203 is configured to train the federal learning model based on historical statistics and/or historical attendance data and historical data obtained from an external data source to obtain a trained federal learning model;
and inputting the current statistical result and/or the current attendance data and the current data acquired from an external data source into the trained federated learning model.
In an embodiment of the present invention, the attendance statistics system 202 is configured to obtain associated data of attendance data from a database; and performing statistical calculation on the attendance data and the associated data according to the target execution code.
As shown in fig. 3, an attendance data privacy calculation method based on a block chain is described in an embodiment of the present invention, taking a block chain-based attendance data privacy calculation system as an example, and includes the following steps:
step 301: the RPA service module acquires attendance checking parameters, checks the attendance checking parameters according to preset parameter rules, acquires attendance checking data from attendance checking equipment according to the attendance checking parameters according to a preset period when the checking is passed, and sends the attendance checking data to an attendance checking statistical system; and when the verification is failed, generating an error message according to the attendance checking parameters, and sending an alarm signal according to the error message.
As shown in fig. 4, the RPA service module includes a parameter obtaining component, a rule matching component, an error alarm component, a data obtaining component and a data sending component, where the data obtaining component includes a timing execution component and an event driving component. It should be noted that the RPA service module further includes an RPA scheduler and an RPA designer, which are not shown in fig. 4.
The parameter acquisition component is used for acquiring attendance parameters; the rule matching component is used for verifying the attendance parameters according to preset parameter rules, transmitting the attendance parameters to the data acquisition component when the verification is passed, generating error messages according to the attendance parameters when the verification is failed, and transmitting the error messages to the error alarm component; the error alarm component is used for sending out an alarm signal according to the error message; the timing execution component is used for acquiring attendance data from the attendance equipment according to the attendance parameters according to a preset period, the event driving component is used for monitoring whether a preset trigger event occurs or not, and if yes, the event driving component acquires the attendance data from the attendance equipment according to the attendance parameters. The data sending component is used for sending the attendance data to the attendance statistical system.
In a practical application scenario, the data acquisition component may only include a timing execution component or an event-driven component, and may also include other execution components. If the data acquisition component cannot acquire attendance data, an error message can be generated according to attendance parameters and sent to the error alarm component, and the error alarm component sends out an alarm signal according to the error message.
The rule matching component can comprise a date matching rule, a staff matching rule and the like, wherein the date matching rule is used for verifying the date in the attendance checking parameters, and the staff matching rule is used for verifying the staff in the attendance checking parameters.
Step 302: the attendance statistics system displays a plurality of attendance sub rules and code segments of the attendance sub rules, selects a plurality of target attendance sub rules from the plurality of attendance sub rules according to the triggering operation of a user, and generates target execution codes corresponding to the target attendance sub rules according to the code segments of the plurality of target attendance sub rules.
Step 303: and acquiring the associated data of the attendance data from the database, and performing statistical calculation on the attendance data and the associated data according to the target execution code to obtain a statistical result.
As shown in fig. 5, the attendance rule subsystem includes a data interface module, a database management module, and an attendance rule module. The attendance rule module is used for displaying a plurality of attendance sub rules and code segments of the attendance sub rules, selecting a plurality of target attendance sub rules from the plurality of attendance sub rules according to the triggering operation of a user, and generating target execution codes corresponding to the target attendance sub rules according to the code segments of the plurality of target attendance sub rules. The data interface module is used for acquiring the associated data of the attendance data from the database of the database management module, and performing statistical calculation on the attendance data and the associated data according to the target execution code to obtain a statistical result. The data represents attendance data, the DB data represents associated data, the subcule represents an attendance sub rule, the rule represents an attendance rule, the snippet represents a code segment corresponding to the attendance sub rule, and the code represents an execution code corresponding to the attendance rule. The user can select the code segment corresponding to the specific attendance rule through a front-end component page provided by the attendance rule subsystem.
The code segment is a specific function for converting and calculating a specific field of attendance data, and various types of code segments can be preset in the attendance rule module. The execution code is a combination of a group of code segments and is used for realizing the overall conversion and calculation of attendance data. The attendance rules are abstract descriptions of the execution code and indicate rules for calculating attendance data.
For example, the attendance data of the employee on the day is as follows:
employee attendance data =: (last) drawing
XXX, attendance time 08:34, attendance result normal, daily result yes,
the name is YYY, the office time of card punching is 08:55, the office result of card punching is normal, and the daily result of card punching is as follows: if not, the user can not select the specific application,
the name is ZZZ, the attendance checking time is 09:02, the attendance checking result is late arrival, the daily report result is: it is that,
……}
wherein, selecting code segment of 'office card punching result statistics' to respectively count the number of two states of office card punching results in data; the number of two states of the daily report result can be counted by selecting the code segment of daily report result counting. By combining the code segments of the 'attendance card result statistics' and the 'daily report result statistics', the execution codes of 'on-the-day card punching and daily report statistics' can be obtained, and attendance data of all employees on the day is counted. The attendance rules in this example are "keep statistics of all employees' attendance check-in status and diary fill-in status".
In an actual application scenario, the attendance data and the associated data are subjected to statistical calculation according to the target execution code, and can be executed by the attendance rule module, and at this time, the processing of the attendance data in the attendance rule module can include the following four processes:
1) and (5) a code segment combining process. Selecting code segments from a built-in code segment library according to a specific attendance rule to combine to obtain an execution code corresponding to the specific attendance rule;
2) a code save process is performed. Storing the execution code in a binary group (attendance rule, execution code) form;
3) a code calling procedure is performed. Sending the attendance data as input to an execution code to obtain a corresponding statistical result;
4) and (5) adding a code segment. By means of adding code segments in the built-in library, the calculation requirement of new attendance rules can be supported.
According to the embodiment of the invention, the workload of recalculating the statistical result when the attendance rule changes is reduced by a mode of combining the built-in code segments into the execution code according to the attendance rule. The programmable attendance rule module can realize the mode of rapid conversion of attendance rules and coexistence of various attendance rules, and improves the usability and the usability of the system.
The generated execution codes can be stored in the attendance rule module and can be displayed to the user subsequently, the user can directly select from the execution codes, and if the displayed execution codes do not meet the requirements, the execution codes are obtained through the combined code segments.
Step 304: determining the identification of the target intelligent contract and the operation type of the statistical result, calling the target intelligent contract which is deployed in a block chain in advance according to the identification of the target intelligent contract, packaging the statistical result and the attendance data into blocks, and adding a timestamp to the blocks.
The operation type is new or updated.
Step 305: and sequencing the blocks formed by packaging the plurality of statistical results, and adding the plurality of blocks into a block chain according to the sequencing result and the operation type.
The target attendance rules are composed of a plurality of attendance rules.
As shown in fig. 6, the blockchain core subsystem includes a contract execution module, an order uplink module, and a blockchain query module.
And the contract execution module is used for determining the identification of the target intelligent contract and the operation type of the statistical result, calling the target intelligent contract which is pre-deployed in the block chain according to the identification of the target intelligent contract, packaging the statistical result and the attendance data into blocks, and adding a timestamp to the blocks. And the sequencing uplink module is used for sequencing the blocks formed by packaging the plurality of statistical results and adding the plurality of blocks into the block chain according to the sequencing result and the operation type. And the block chain query module is used for querying blocks meeting the conditions on the block chain according to the input block id or the field name and returning the blocks.
Step 306: the privacy calculation system acquires statistical results and attendance data from the blockchain, trains a federal learning model based on the historical statistical results and the historical attendance data and historical data acquired from an external data source to acquire a trained federal learning model, and inputs the current statistical results and the current attendance data and the current data acquired from the external data source into the trained federal learning model to acquire privacy calculation results.
As shown in fig. 7, the privacy computing system includes a data adding module, a data applying module, a privacy computing module, a hardware interface module, a result output module, and a model deriving module. The data adding module is used for obtaining statistical results and/or attendance data from the block chain. And the data application module is used for sending a data use request to an external data source and receiving data returned by the external data source. The system comprises a privacy calculation module, a block chain calculation module and a block checking module, wherein the privacy calculation module is used for executing privacy calculation and is used for acquiring statistical results and attendance checking data from the block chain; and carrying out privacy calculation based on the statistical result, the attendance data and the data acquired from the external data source to obtain a privacy calculation result. And the privacy calculation module calls the hardware interface module to accelerate the privacy calculation in the privacy calculation process. The result output module is used for storing the privacy calculation result and providing a data query return interface, and an external privacy calculation result user can obtain the privacy calculation result through the data query return interface. And the model export module is used for storing the model generated by the privacy calculation module, such as a trained federal learning model, and an external model user can acquire the trained model through a model query return interface and use the model in other prediction scenes.
In fig. 7, pt, pkl, dat, and pb respectively represent model files with different suffix names, and the result output module may output privacy calculation results in json and the like. Techniques used in the privacy computation module (i.e., the privacy computation operator in fig. 7) may include: MPC (Multi-Party computing, secure multiparty computing), FL (Federal Learning), PSI (Private Set interaction, privacy Set Intersection), SS (Secret Share, Secret sharing), GC (Garbled Circuit), OT (Obvious Transfer, Innovation), linrR (linear Regression ), logistic Regression, xgBoost (explicit gradient boosting, gradient Tree), nn (neural network).
The privacy calculation system combines the statistical result obtained by the attendance statistical system with data introduced from an external data source to execute privacy calculation, and the obtained privacy calculation result and the model obtained by training can be applied to scenes such as personnel system training, manager candidate and performance improvement in a company. By introducing commuting, shopping in shopping malls, banking financial data and the like from an external data source, travel paths, financial products and the like can be recommended for employees.
The process of performing privacy calculations based on federal learning is now described by way of a specific example. The method comprises the steps that data acquired from an external data source are road data and weather data, a federal learning model is trained according to attendance data, the road data and the weather data, and the upper limit and the lower limit of the attendance duration of a worker and the tardy probability of the worker are predicted on the basis of the trained federal learning model.
Specifically, historical attendance data of the employee, corresponding historical road data and historical weather data are used as an original data set. The original data set was divided into a training set and a validation set at an 8:2 ratio. And training the federal learning model by using the training set, verifying the accuracy of the federal learning model by using the verification set, and obtaining the trained federal learning model after the model accuracy reaches a preset threshold value.
The attendance data may include: the work time of the employee, the departure time, and the time of the employee to be punched.
The road data may include: the route between the employee's address and the company and the transit time of each route.
The weather data may include: whether it is raining.
Training a federal learning model based on attendance data of the employees every day in the past half year and corresponding road data and weather data, acquiring the departure time of the employees today, the time to be checked, the road data and the weather data specified by a company, and predicting the upper limit and the lower limit of the attendance duration of the employees and the late arrival probability of the employees.
The upper and lower commute time limits and the late arrival probability predicted for different departure times are shown in table 1.
It should be noted that the specific contents of the attendance data and the data acquired from the external data source may be determined according to an actual application scenario, and are not limited to the contents mentioned in the embodiments of the present invention. For example, attendance data may include the attendance time and departure time of the employee, the federal learning model trained based on the attendance data and road data, and upper and lower limits for the length of time the employee commuted based on the trained federal learning model predicted.
TABLE 1
| Departure time | Lower limit/min of commuting time | Commute duration upper limit/min | Probability of late arrival/% |
| 7:35 | 30 | 55 | 0 |
| 7:45 | 50 | 75 | 10 |
| 7:55 | 65 | 95 | 75 |
The embodiment of the invention further excavates the value of the attendance data through privacy calculation, and widens the application scene of the attendance data. The attendance data are stored in the block chain, so that the attendance data are prevented from being tampered, and the privacy safety of the staff is ensured.
The embodiment of the present invention will specifically describe a process for performing privacy computation based on multi-party security computation by using a specific example. For example, in a practical application scenario, the attendance checking and accounting staff needs to count the performance of the department according to the number of people arriving late by the department, meanwhile, the specific number of people arriving late by each department keeps the security of the attendance checking and accounting staff, if the area where the address of the staff is located is notified to perform the regional nucleic acid detection on the same day, and the staff completes the nucleic acid detection on the same day, the staff can delay the card punching for one hour, otherwise, the staff is regarded as arriving late. In this case, as shown in fig. 8, the above-described business problem can be solved using secret sharing and homomorphic encryption techniques. The privacy calculation module comprises a department terminal and an accounting personnel terminal, wherein the department terminal is used by each department attendance data manager, and the accounting personnel terminal is used by a performance accounting personnel.
In the embodiment of the invention, the attendance data is the number of people late in the department, and the data acquired from the external data source is nucleic acid detection information. Wherein the nucleic acid detection information includes: whether the employee detects nucleic acid on the day and whether the region where the employee address is located has a region nucleic acid detection notice.
The specific process of privacy calculation is as follows:
s1: each department decomposes the late number of people and the nucleic acid detection information of the department into n parts of sub-data through secret sharing, and the late number of people or the nucleic acid detection information of the department can be restored by holding the n parts of sub-data; respectively executing S2 and S3 aiming at the number of people arriving late in the department and the subdata corresponding to the nucleic acid detection information;
s2: sending the n-1 parts of sub data to other departments of the company;
s3: after the nth sub data of the local department and the sub data of other departments held by each department are subjected to homomorphic encryption processing, the encrypted data are sent to attendance checking and accounting personnel;
s4: the attendance checking and accounting personnel respectively restore the encrypted data corresponding to the late arrival number and the nucleic acid detection information of the department at an accounting personnel end by using secret sharing, calculate under the homomorphic condition based on the restored encrypted data and the performance assessment mode to obtain the encrypted data of the performance of each department, decrypt the encrypted data by homomorphic encryption to obtain the plaintext data of the performance of each department, wherein the plaintext data is the privacy calculation result based on the multi-party safety calculation.
Referring now to FIG. 9, shown is a block diagram of a computer system 900 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the use range of the embodiment of the present invention.
As shown in fig. 9, the computer system 900 includes a Central Processing Unit (CPU) 901 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the system 900 are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
The following components are connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The above-described functions defined in the system of the present invention are executed when the computer program is executed by a Central Processing Unit (CPU) 901.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a sending module, an obtaining module, a determining module, and a first processing module. The names of these modules do not form a limitation on the modules themselves in some cases, and for example, the sending module may also be described as a "module sending a picture acquisition request to a connected server".
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A block chain-based attendance data privacy calculation method is characterized by comprising the following steps:
the RPA service module acquires attendance checking parameters, checks the attendance checking parameters according to preset parameter rules, acquires attendance checking data according to the attendance checking parameters if the checking is passed, and sends the attendance checking data to an attendance statistical system;
the attendance statistical system generates a target execution code corresponding to a target attendance rule according to a preset code fragment corresponding to an attendance rule, performs statistical calculation on the attendance data according to the target execution code to obtain a statistical result, and uploads the statistical result and the attendance data to a block chain; the target attendance rules are composed of a plurality of attendance sub rules;
the privacy computing system acquires the statistical result and/or the attendance data from the blockchain; and carrying out privacy calculation based on the statistical result and/or the attendance data and data acquired from an external data source to obtain a privacy calculation result.
2. The method of claim 1, further comprising:
and if the verification fails, the RPA service module generates an error message according to the attendance checking parameters and sends out an alarm signal according to the error message.
3. The method of claim 1,
acquiring attendance data according to the attendance parameters, comprising:
acquiring the attendance data from attendance equipment according to the attendance parameters according to a preset period;
and/or the presence of a gas in the gas,
acquiring attendance data according to the attendance parameters, wherein the attendance data comprises the following steps:
and monitoring whether a preset trigger event occurs, and if so, acquiring the attendance data from attendance equipment according to the attendance parameters.
4. The method of claim 1,
the attendance statistic system generates a target execution code corresponding to a target attendance rule according to a preset code fragment corresponding to the attendance rule, and the target execution code comprises the following steps:
the attendance statistics system displays a plurality of attendance sub rules and code fragments of the attendance sub rules, selects a plurality of target attendance sub rules from the plurality of attendance sub rules according to the triggering operation of a user, and generates target execution codes corresponding to the target attendance sub rules according to the code fragments of the plurality of target attendance sub rules;
the target attendance rules are composed of the plurality of target attendance rules.
5. The method of any one of claims 1 to 4,
uploading the statistical result and the attendance data to a block chain, including:
determining the identification of the target intelligent contract and the operation type of the statistical result;
calling a target intelligent contract which is deployed in advance in the block chain according to the identification of the target intelligent contract, packaging the statistical result and the attendance data into a block, and adding a timestamp to the block;
sequencing blocks formed by packaging a plurality of statistical results;
and adding a plurality of blocks into the block chain according to the sorting result and the operation type.
6. The method of claim 1,
performing privacy calculation based on the statistical result and/or the attendance data and data obtained from an external data source, including:
and calling a trusted execution environment and/or a graphic processor and/or a field programmable gate array to execute privacy calculation based on the statistical result and/or the attendance data and data acquired from an external data source.
7. The method of claim 1,
performing privacy calculation based on the statistical result and/or the attendance data and data obtained from an external data source, including:
based on the multi-party security calculation, performing privacy calculation on the statistical result and/or the attendance data and data acquired from an external data source.
8. The method of claim 1,
performing privacy calculation based on the statistical result and/or the attendance data and data obtained from an external data source, including:
training a federal learning model based on historical statistical results and/or historical attendance data and historical data acquired from an external data source to obtain a trained federal learning model;
and inputting the current statistical result and/or the current attendance data and the current data acquired from an external data source into the trained federated learning model.
9. The method of claim 1, further comprising:
acquiring the associated data of the attendance data from a database;
performing statistical calculation on the attendance data according to the target execution code, wherein the statistical calculation comprises the following steps:
and performing statistical calculation on the attendance data and the associated data according to the target execution code.
10. A blockchain-based attendance data privacy computing system, comprising: the system comprises an RPA service module, an attendance statistic system and a privacy computing system;
the RPA service module is used for acquiring attendance parameters, verifying the attendance parameters according to preset parameter rules, acquiring attendance data according to the attendance parameters if verification is passed, and sending the attendance data to the attendance statistical system;
the attendance statistical system is used for generating a target execution code corresponding to a target attendance rule according to a preset code fragment corresponding to an attendance rule, performing statistical calculation on the attendance data according to the target execution code to obtain a statistical result, and uploading the statistical result and the attendance data to a block chain; the target attendance rules consist of a plurality of attendance sub rules;
the privacy computing system is used for acquiring the statistical result and/or the attendance data from the block chain; and carrying out privacy calculation based on the statistical result and/or the attendance data and data acquired from an external data source to obtain a privacy calculation result.
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