Collaborative storage method and system for cashier data

Abstract

The invention discloses a collaborative storage method and system for cashier data, and is applied to the technical field of data processing, and the method comprises the steps: obtaining a cashier data storage instruction according to a cashier terminal, correcting a target cashier data source based on an error self-adaptive correction mechanism, and obtaining a corrected cashier data source. And performing data arrangement on the corrected cashier data source according to a multilateral coupling arrangement channel to generate a standard cashier data source, the standard cashier data source comprising a plurality of standard cashier data streams. And performing linkage encryption modulation on the standard cashier data source according to an encryption modulation model and an encryption modulation record source to obtain an encrypted cashier data source. And storing the plurality of encrypted cashier data streams to a cloud edge collaborative storage channel based on a cloud edge collaborative storage algorithm. The technical problems that in the prior art, a cashier data storage method is low in data storage safety, and cashier data are prone to being lost or tampered are solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a collaborative storage method and system for cash register data. Background Art

[0002] In the retail industry, cash register data is the final proof of commodity transactions and an important proof of commodity transactions. However, in the prior art, cash register data is stored in local storage devices or the cash register device itself. When the device fails, it is very easy to cause the loss of cash register data, and the security of data storage is low.

[0003] Therefore, in the prior art, the cashier data storage method has low data security, which can easily lead to technical problems such as cashier data being lost or tampered with. Summary of the invention

[0004] The present application provides a collaborative storage method and system for cashier data, thereby solving the technical problem that the cashier data storage method in the prior art has low data security and is very likely to cause the cashier data to be lost or tampered with.

[0005] The present application provides a collaborative storage method for cashier data, the method comprising: obtaining a cashier data storage instruction according to a cashier terminal, wherein the cashier data storage instruction comprises a target cashier data source, and the target cashier data source comprises a plurality of cashier data streams to be stored; correcting the target cashier data source based on an error adaptive correction mechanism to obtain a corrected cashier data source, wherein the corrected cashier data source comprises a plurality of corrected cashier data streams; sorting the corrected cashier data source according to a multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source comprises a plurality of standard cashier data streams; performing linkage encryption modulation on the standard cashier data source according to an encryption modulation model and an encryption modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source comprises a plurality of encrypted cashier data streams; storing the plurality of encrypted cashier data streams in a cloud-edge collaborative storage channel based on a cloud-edge collaborative storage algorithm, wherein the cloud-edge collaborative storage channel comprises a cloud storage unit and an edge storage unit.

[0006] The present application also provides a collaborative storage system for cashier data, the system comprising: a storage instruction acquisition module, used to obtain a cashier data storage instruction according to a cashier terminal, wherein the cashier data storage instruction includes a target cashier data source, and the target cashier data source includes multiple cashier data streams to be stored; a data correction module, used to correct the target cashier data source based on an error adaptive correction mechanism to obtain a corrected cashier data source, wherein the corrected cashier data source includes multiple corrected cashier data streams; a data sorting module, used to sort the corrected cashier data source according to a multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes multiple standard cashier data streams; an encryption module, used to perform linkage encryption modulation on the standard cashier data source according to an encryption modulation model and an encryption modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source includes multiple encrypted cashier data streams; a storage module, used to store the multiple encrypted cashier data streams in a cloud-edge collaborative storage channel based on a cloud-edge collaborative storage algorithm, wherein the cloud-edge collaborative storage channel includes a cloud storage unit and an edge storage unit.

[0007] The collaborative storage method and system of cashier data proposed in this application are to obtain cashier data storage instructions according to the cashier terminal, and to correct the target cashier data source based on the error adaptive correction mechanism to obtain the corrected cashier data source. The corrected cashier data source is sorted according to the multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes multiple standard cashier data streams. The standard cashier data source is linked encrypted and modulated according to the encryption modulation model and the encryption modulation record source to obtain an encrypted cashier data source. The multiple encrypted cashier data streams are stored in the cloud-edge collaborative storage channel based on the cloud-edge collaborative storage algorithm. The secure storage of the cashier data on the cloud and edge is realized, and the security of the data is guaranteed, thereby preventing the data from being lost or tampered with. The technical problem that the cashier data storage method in the prior art has low data security and is very likely to cause the loss or tampering of the cashier data is solved.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings of the embodiments of the present invention. Obviously, the drawings in the following description only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0010] Figure 1 A schematic diagram of a process of a collaborative storage method for cashier data provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for generating a corrected cashier data source by a collaborative storage method for cashier data provided in an embodiment of the present application; Figure 3 A schematic diagram of a process for generating an encrypted cashier data source by a collaborative storage method for cashier data provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a collaborative storage system for cashier data provided in an embodiment of the present application; Explanation of the reference numerals: storage instruction acquisition module 11, data correction module 12, data sorting module 13, encryption module 14, storage module 15. DETAILED DESCRIPTION

[0011] Embodiment 1 In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0012] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0013] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0015] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, the modules are illustrative only, and different aspects of the system and method may use different modules.

[0016] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0017] like Figure 1 As shown, an embodiment of the present application provides a collaborative storage method for cash register data, the method comprising: According to the cashier terminal, a cashier data storage instruction is obtained, wherein the cashier data storage instruction includes a target cashier data source, and the target cashier data source includes a plurality of cashier data streams to be stored; Correcting the target cashier data source based on an error adaptive correction mechanism to obtain a corrected cashier data source, wherein the corrected cashier data source includes a plurality of corrected cashier data streams; The corrected cashier data source is sorted according to the multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes a plurality of standard cashier data streams; According to the cashier terminal, a cashier data storage instruction is obtained, wherein the cashier data storage instruction includes a target cashier data source, the target cashier data source includes multiple cashier data streams to be stored and corresponding encryption levels, the target cashier data source is the cashier data generated by the cashier terminal during the cashier process, and each cashier data stream to be stored corresponds to the cashier data generated by a cashier terminal. Based on the error adaptive correction mechanism, the target cashier data source is corrected to obtain a corrected cashier data source, wherein the corrected cashier data source includes multiple corrected cashier data streams. The corrected cashier data source is data sorted according to the multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes multiple standard cashier data streams.

[0018] like Figure 2 As shown, the method provided in the embodiment of the present application also includes: Loading source device status monitoring data and source device environment monitoring data corresponding to each cashier data stream to be stored in the target cashier data source; Performing anomaly detection on the source device state monitoring data and the source device environment monitoring data respectively to obtain device state anomaly detection results and device environment anomaly detection results; Performing device abnormality depth evaluation according to the device state abnormality detection result and the device environment abnormality detection result to obtain a device abnormality depth coefficient; Determining whether the device abnormality depth coefficient is less than a device abnormality depth threshold; If the device abnormality depth coefficient is greater than / equal to the device abnormality depth threshold, the target cashier data source is corrected according to the device state abnormality detection result and the device environment abnormality detection result mapping to generate the corrected cashier data source.

[0019] Load the source device status monitoring data and source device environment monitoring data corresponding to each cashier data stream to be stored in the target cashier data source, wherein the source device status monitoring data is the network connection environment, device status, etc. of the device, such as connection delay, number of connection interruptions, and device maintenance records, such as the number of input keyboard maintenance, etc., and the source device environment monitoring data is the ambient temperature of the device. Perform abnormality detection on the source device status monitoring data and the source device environment monitoring data respectively to obtain device status abnormality detection results and device environment abnormality detection results. Perform device abnormality depth evaluation based on the device status abnormality detection results and the device environment abnormality detection results to obtain the device abnormality depth coefficient. When performing the device abnormality depth evaluation, obtain the device status abnormality detection results and the device environment abnormality detection results, and compare them with the corresponding detection standard values ​​respectively to obtain the difference between the detection results and the corresponding detection standard values. The detection standard values ​​are the device environment and device status parameters when the device is working normally. The acquired difference is input into the abnormal depth coefficient comparison table to obtain the abnormal depth coefficient. The abnormal depth coefficient comparison table contains the difference between each test result and the corresponding test standard value, and the preset corresponding abnormal depth coefficient. For example, the abnormal depth coefficient corresponding to the ambient temperature difference of 1 is 10. The specific corresponding coefficient is set according to the actual situation. The higher the impact of the difference on the normal operation of the equipment, the larger the corresponding abnormal depth coefficient. According to the difference calculation result, the abnormal depth coefficients of all equipment state abnormality detection results and the equipment environment abnormality detection results are obtained, and added to obtain the equipment abnormality depth coefficient. Further, it is determined whether the equipment abnormality depth coefficient is less than the equipment abnormality depth threshold. The equipment abnormality depth threshold is the threshold with the lowest abnormal risk of the equipment running in the corresponding environment set in advance. When it is greater than or equal to this threshold, the abnormal risk of the corresponding operating environment is higher. If the equipment abnormality depth coefficient is greater than / equal to the equipment abnormality depth threshold, the target cashier data source is corrected according to the mapping of the equipment state abnormality detection result and the equipment environment abnormality detection result to generate the corrected cashier data source.

[0020] The method provided in the embodiment of the present application also includes: Retrieving a cashier data compensation record set, and training a cashier data compensation unit that meets a preset compensation accuracy constraint according to the cashier data compensation record set; Based on the cashier data compensation unit, an abnormality compensation analysis is performed on the device state abnormality detection result and the device environment abnormality detection result to obtain initial abnormality compensation data; Sending the initial abnormal compensation data to the cashier machine interaction interface and receiving compensation interaction feedback data; The compensation interaction feedback data and the initial abnormal compensation data are data-fused to generate optimized abnormal compensation data, and the target cashier data source is mapped and corrected according to the optimized abnormal compensation data to obtain the corrected cashier data source.

[0021] According to the mapping of the device state abnormality detection result and the device environment abnormality detection result, the target cashier data source is corrected to generate the corrected cashier data source, including: retrieving the cashier data compensation record set, wherein the cashier data compensation record set records the cashier compensation data and the corresponding abnormality detection result and the device environment abnormality detection result in history. And according to the cashier data compensation record set, a cashier data compensation unit that meets the preset compensation accuracy constraint is trained, that is, the cashier data compensation record set is used as training data to supervise the neural network model until the compensation data finally output by the model meets the preset accuracy rate. The training of the model is completed, and the cashier data compensation unit is obtained. At the same time, the cashier data compensation unit also includes a corresponding compensation accuracy constraint module, which is used to retain and remove valid digits of the final data result. The higher the preset compensation accuracy, the more valid digits are corresponding. Based on the cashier data compensation unit, the device state abnormality detection result and the device environment abnormality detection result are input into the cashier data compensation unit for abnormal compensation analysis to obtain initial abnormal compensation data, because the initial abnormal compensation data generated at this time is based on the cashier data compensation unit. Therefore, it is necessary to send the initial abnormal compensation data to the cashier machine interaction interface for manual confirmation of interactive compensation, manually confirm and obtain the compensation interactive feedback data to ensure the accuracy of the data, receive the compensation interactive feedback data, and the compensation interactive feedback data contains the compensation data of the corresponding abnormal data position of the manual feedback. Finally, the compensation interactive feedback data and the initial abnormal compensation data are data fused, that is, the compensation interactive feedback data of the corresponding data position of the initial abnormal compensation data is data compensated to generate optimized abnormal compensation data, and the target cashier data source is mapped and corrected according to the optimized abnormal compensation data to obtain the corrected cashier data source.

[0022] The method provided in the embodiment of the present application also includes: The multi-side coupling sorting channel includes a pre-processing sorting channel, a compression sorting channel and a sorting verification channel; According to the corrected cashier data source, extracting the e-th corrected cashier data stream, wherein e is a positive integer, e belongs to E, and E is the total number of data streams of the corrected cashier data source; Inputting the e-th corrected cashier data stream into the preprocessing and sorting channel to generate an e-th preprocessed cashier data stream, wherein the preprocessing and sorting channel includes preset cashier data constraint rules; Compressing the e-th pre-processed cashier data stream according to the compression and sorting channel to generate an e-th compressed cashier data stream; Compare and analyze the e-th pre-processed cashier data stream and the e-th compressed cashier data stream according to the sorting and verification channel to generate an e-th compression verification index; Determining whether the e-th compression verification index satisfies the compression verification constraint; If the e-th compression verification index satisfies the compression verification constraint, the e-th compressed cashier data stream is set as the e-th standard cashier data stream, and the e-th standard cashier data stream is added to the standard cashier data source.

[0023] The correction cashier data source is sorted according to the multilateral coupling sorting channel to generate a standard cashier data source, including: the multilateral coupling sorting channel includes a preprocessing sorting channel, a compression sorting channel and a sorting verification channel. According to the correction cashier data source, the e-th correction cashier data stream is extracted, wherein e is a positive integer, e belongs to E, and E is the total number of data streams of the correction cashier data source. The e-th correction cashier data stream is input into the preprocessing sorting channel to generate the e-th preprocessing cashier data stream, wherein the preprocessing sorting channel includes preset cashier data constraint rules, and the preset cashier data constraint rules are pre-set data processing rules, including: unified data format, missing data filling with zeros, etc., to facilitate subsequent further processing. The e-th preprocessing cashier data stream is compressed according to the compression sorting channel, wherein the compression method adopts a commonly used data compression method to generate the e-th compressed cashier data stream. According to the sorting and verification channel, the e-th pre-processed cashier data stream and the e-th compressed cashier data stream are compared and analyzed. The sorting and verification channel decompresses the e-th compressed cashier data stream and calculates the similarity with the e-th pre-processed cashier data stream to obtain the final similarity calculation result and generate the e-th compression verification index. It is judged whether the e-th compression verification index meets the compression verification constraint, wherein the compression verification constraint is a preset similarity threshold between the compressed data and the pre-compression data. When it is greater than the threshold, the compression effect is good, otherwise the compression effect is poor, and other compression methods need to be used for compression. If the e-th compression verification index meets the compression verification constraint, that is, it is greater than or equal to the compression verification constraint, the e-th compressed cashier data stream is set as the e-th standard cashier data stream, and the e-th standard cashier data stream is added to the standard cashier data source.

[0024] Perform linkage encryption modulation on the standard cashier data source according to the encryption modulation model and the encryption modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source includes a plurality of encrypted cashier data streams; Based on the cloud-edge collaborative storage algorithm, the multiple encrypted cashier data streams are stored in a cloud-edge collaborative storage channel, wherein the cloud-edge collaborative storage channel includes a cloud storage unit and an edge storage unit.

[0025] The standard cashier data source is linked encrypted and modulated according to the encryption modulation model and the encryption modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source includes multiple encrypted cashier data streams, and each encrypted cashier data stream corresponds to a standard cashier data stream. Finally, based on the cloud-edge collaborative storage algorithm, the multiple encrypted cashier data streams are stored in the cloud-edge collaborative storage channel, wherein the cloud-edge collaborative storage channel includes a cloud storage unit and an edge storage unit. The secure storage of the cashier data on the cloud and the edge is realized, the security of the data is guaranteed, and thus the data is prevented from being lost or tampered with.

[0026] like Figure 3 As shown, the method provided in the embodiment of the present application also includes: Perform encryption level identification according to the plurality of standard cashier data streams to generate a plurality of encryption level coefficients; Based on the multiple encryption level coefficients, the multiple standard cashier data streams are assigned encryption schemes according to the encryption modulation model to generate multiple matching encryption schemes; Performing confidence security verification on the multiple matching encryption schemes based on the encrypted modulation record source to obtain multiple verified encryption schemes; The plurality of standard cashier data streams are encrypted based on the plurality of verification encryption schemes to generate the encrypted cashier data source.

[0027] The encryption level is identified according to the multiple standard cashier data streams, wherein the encryption level is the encryption level preset for the standard cashier data stream, the encryption level corresponds to the encryption level coefficient, and multiple encryption level coefficients are generated according to the encryption level. Based on the multiple encryption level coefficients, the multiple standard cashier data streams are assigned encryption schemes according to the encryption modulation model to generate multiple matching encryption schemes, and different encryption level coefficients correspond to a fixed number of generated encryption schemes. In the encryption modulation model, different encryption level coefficients and corresponding encoding methods are combined to finally form a fixed number of encryption schemes. Based on the encryption modulation record source, the multiple matching encryption schemes are trusted and securely verified to obtain multiple verification encryption schemes; based on the multiple verification encryption schemes, the multiple standard cashier data streams are encrypted to generate the encrypted cashier data source.

[0028] The method provided in the embodiment of the present application also includes: Traversing the multiple matching encryption schemes and extracting a first matching encryption scheme; Performing a global confidence calculation on the first matching encryption scheme based on the encrypted modulation record source to generate a first global encryption confidence coefficient; Performing local confidence calculation on the first matching encryption scheme based on the encrypted modulation record source to generate a plurality of first local confidence coefficients; Generate a first local encrypted confidence coefficient, wherein the first local encrypted confidence coefficient is obtained by performing mean calculation on the multiple first local confidence coefficients; Determining whether the first global encryption confidence coefficient and the first local encryption confidence coefficient satisfy a bilateral confidence security verification interval; If the first global encryption confidence coefficient and the first local encryption confidence coefficient satisfy the bilateral confidence security verification interval, using the first matching encryption scheme as a first verification encryption scheme; The encrypted modulation recording source is updated according to the first authentication encryption scheme, and the first authentication encryption scheme is added to the plurality of authentication encryption schemes.

[0029] Based on the encrypted modulation record source, the multiple matching encryption schemes are verified for confidence security to obtain multiple verified encryption schemes, including: extracting a first matching encryption scheme by traversing the multiple matching encryption schemes, the first matching encryption scheme being one of the multiple matching encryption schemes. Based on the encrypted modulation record source, a global confidence calculation is performed on the first matching encryption scheme to generate a first global encryption confidence coefficient, wherein the encrypted modulation record source is a record of the encryption scheme adopted in the historical encryption process. When performing the global confidence calculation, the first global encryption confidence coefficient is generated by obtaining the frequency of adoption of the first matching encryption scheme in the encrypted modulation record source, obtaining the ratio of the frequency of adoption to the frequency of adoption of each encryption scheme in the encrypted modulation record source. Based on the encrypted modulation record source, a local confidence calculation is performed on the first matching encryption scheme to generate multiple first local confidence coefficients. When performing the local confidence calculation, multiple first local confidence coefficients are generated by obtaining the ratio of the frequency of occurrence of each encoding method in the first matching encryption scheme in the encrypted modulation record source to the average frequency of each encoding method. And the calculated ratio of each encoding method in each first matching encryption scheme, that is, multiple first local confidence coefficients, is averaged to generate a first local encryption confidence coefficient. Generate a first local encryption confidence coefficient, wherein the first local encryption confidence coefficient is obtained by averaging the multiple first local confidence coefficients. Further, determine whether the first global encryption confidence coefficient and the first local encryption confidence coefficient meet the bilateral confidence security verification interval, wherein the bilateral confidence security verification interval is a preset preferred interval of the global encryption confidence coefficient and the local encryption confidence coefficient. When the first global encryption confidence coefficient and the first local encryption confidence coefficient are both within the bilateral confidence security verification interval, the current encryption scheme meets the requirements, otherwise, continue to obtain an encryption scheme for confidence security verification. If the first global encryption confidence coefficient and the first local encryption confidence coefficient meet the bilateral confidence security verification interval, the first matching encryption scheme is used as the first verification encryption scheme, the encrypted modulation record source is updated according to the first verification encryption scheme, and the first verification encryption scheme is added to the multiple verification encryption schemes.

[0030] The method provided in the embodiment of the present application also includes: Obtaining multiple individual target capacities corresponding to the multiple encrypted cashier data streams, and calculating a target global capacity according to the multiple individual target capacities; Collecting redundant storage capacity parameters of each cloud storage module in the cloud storage unit to obtain multiple cloud redundant storage capacities, and calculating a global cloud redundant capacity based on the multiple cloud redundant storage capacities; Determining whether the target global capacity is less than the global cloud redundancy capacity; If the target global capacity is less than the global cloud redundant capacity, generating a cloud storage instruction; Based on the cloud storage instruction, the plurality of cloud redundant storage amounts are used as cloud storage constraints, and the plurality of single target capacities are used as cloud storage targets; Perform cloud storage analysis according to the cloud storage constraints and the cloud storage target, determine a cloud storage solution, and store the multiple encrypted cashier data streams in the cloud storage unit according to the cloud storage solution.

[0031] Based on the cloud edge collaborative storage algorithm, the multiple encrypted cashier data streams are stored in the cloud edge collaborative storage channel, including: obtaining multiple single target capacities corresponding to the multiple encrypted cashier data streams, that is, obtaining the required capacities of the multiple encrypted cashier data streams, and summing up the target global capacity according to the multiple single target capacities to obtain the overall required capacity. Collecting the redundant storage capacity parameters of each cloud storage module in the cloud storage unit, the cloud storage unit includes multiple cloud storage modules for storing data, and the redundant storage capacity parameters are the remaining storage capacity of each storage module. Obtaining multiple cloud redundant storage capacities, and summing up the global cloud redundant capacity according to the multiple cloud redundant storage capacities. Determining whether the target global capacity is less than the global cloud redundant capacity. If the target global capacity is less than the global cloud redundant capacity, generating a cloud storage instruction to perform cloud storage on the multiple encrypted cashier data streams. Based on the cloud storage instruction, the multiple cloud redundant storage capacities are used as cloud storage constraints, and the multiple single target capacities are used as cloud storage targets. Perform cloud storage analysis according to the cloud storage constraints and the cloud storage target, store the cloud storage target in a cloud storage module that meets the cloud storage constraints, determine a cloud storage plan, and store the multiple encrypted cashier data streams in the cloud storage unit according to the cloud storage plan.

[0032] The method provided in the embodiment of the present application also includes: If the target global capacity is greater than / equal to the global cloud redundant capacity, generating an edge storage instruction; According to the edge storage instruction, redundant storage information of each edge storage node in the edge storage unit is collected to obtain multiple edge redundant storage amounts, and a global edge redundant capacity is calculated according to the multiple edge redundant storage amounts; Determining whether the global edge redundancy capacity is greater than / equal to the target global capacity; If the global edge redundant capacity is greater than / equal to the target global capacity, the plurality of edge redundant storage amounts are used as edge storage constraints, and the plurality of monomer target capacities are used as edge storage targets; Perform edge storage allocation according to the edge storage constraint and the edge storage target, and generate multiple initial edge storage solutions; According to the edge node position distribution corresponding to each edge storage node, traverse the multiple initial edge storage schemes to calculate multiple edge storage distances; Based on the multiple edge storage distances, the multiple initial edge storage solutions are screened to obtain a matching edge storage solution, and the multiple encrypted cashier data streams are stored in the edge storage unit according to the matching edge storage solution.

[0033] Determining whether the target global capacity is less than the global cloud redundant capacity also includes: if the target global capacity is greater than / equal to the global cloud redundant capacity, generating an edge storage instruction. According to the edge storage instruction, the redundant storage information of each edge storage node in the edge storage unit is collected, the edge storage unit includes multiple edge storage nodes, multiple edge redundant storage amounts are obtained, and the edge redundant storage amount is the remaining storage amount of the edge storage node, and the global edge redundant capacity is calculated by adding the multiple edge redundant storage amounts. Determining whether the global edge redundant capacity is greater than / equal to the target global capacity. If the global edge redundant capacity is greater than / equal to the target global capacity, the multiple edge redundant storage amounts are used as edge storage constraints, and the multiple monomer target capacities are used as edge storage targets. Edge storage allocation is performed according to the edge storage constraints and the edge storage targets, that is, multiple initial edge storage schemes are generated based on the condition that the edge storage target satisfies the edge storage constraints. According to the edge node position distribution corresponding to each edge storage node, the multiple initial edge storage schemes are traversed to calculate multiple edge storage distances. When the data transmission distance is longer, the probability of data loss is higher. Based on the multiple edge storage distances, the multiple initial edge storage solutions are screened to obtain a matching edge storage solution, that is, to obtain a storage solution corresponding to the smallest edge storage distance among the multiple initial edge storage solutions, and the multiple encrypted cashier data streams are stored in the edge storage unit according to the matching edge storage solution. If the global edge redundant capacity is less than the target global capacity, an edge optimization instruction is generated, and the edge redundant deviation capacity is calculated according to the global edge redundant capacity and the target global capacity, and the edge redundant deviation capacity is the difference between the target global capacity and the global edge redundant capacity. The edge storage unit is optimized for edge nodes according to the edge redundant deviation capacity, and non-important cache data stored in the edge node is deleted.

[0034] The technical solution provided by the embodiment of the present invention is to obtain a cashier data storage instruction according to a cashier terminal, wherein the cashier data storage instruction includes a target cashier data source, and the target cashier data source includes multiple cashier data streams to be stored; the target cashier data source is corrected based on an error adaptive correction mechanism to obtain a corrected cashier data source, wherein the corrected cashier data source includes multiple corrected cashier data streams; the corrected cashier data source is sorted according to a multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes multiple standard cashier data streams; the standard cashier data source is linked encrypted and modulated according to an encrypted modulation model and an encrypted modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source includes multiple encrypted cashier data streams; based on a cloud-edge collaborative storage algorithm, the multiple encrypted cashier data streams are stored in a cloud-edge collaborative storage channel, wherein the cloud-edge collaborative storage channel includes a cloud storage unit and an edge storage unit. The secure storage of the cloud and edge ends of the cashier data is realized, and the security of the data is guaranteed, thereby preventing the data from being lost or tampered with. The invention solves the technical problem that the data storage security of the cash register data storage method in the prior art is low and the cash register data is easily lost or tampered with.

[0035] Embodiment 2 Based on the same inventive concept as the collaborative storage method for cashier data in the aforementioned embodiment, the present invention also provides a collaborative storage system for cashier data. The system can be implemented in hardware and / or software, and can generally be integrated into an electronic device to execute the method provided by any embodiment of the present invention. Figure 4 As shown, the system comprises: A storage instruction acquisition module 11 is used to obtain a cashier data storage instruction according to a cashier terminal, wherein the cashier data storage instruction includes a target cashier data source, and the target cashier data source includes a plurality of cashier data streams to be stored; A data correction module 12, configured to correct the target cashier data source based on an error adaptive correction mechanism to obtain a corrected cashier data source, wherein the corrected cashier data source includes a plurality of corrected cashier data streams; A data sorting module 13 is used to sort the corrected cashier data source according to the multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes a plurality of standard cashier data streams; The encryption module 14 is used to perform linkage encryption modulation on the standard cashier data source according to the encryption modulation model and the encryption modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source includes a plurality of encrypted cashier data streams; The storage module 15 is used to store the multiple encrypted cashier data streams into a cloud-edge collaborative storage channel based on a cloud-edge collaborative storage algorithm, wherein the cloud-edge collaborative storage channel includes a cloud storage unit and an edge storage unit.

[0036] Furthermore, the data correction module 12 is also used for: Loading source device status monitoring data and source device environment monitoring data corresponding to each cashier data stream to be stored in the target cashier data source; Performing anomaly detection on the source device state monitoring data and the source device environment monitoring data respectively to obtain device state anomaly detection results and device environment anomaly detection results; Performing device abnormality depth evaluation according to the device state abnormality detection result and the device environment abnormality detection result to obtain a device abnormality depth coefficient; Determining whether the device abnormality depth coefficient is less than a device abnormality depth threshold; If the device abnormality depth coefficient is greater than / equal to the device abnormality depth threshold, the target cashier data source is corrected according to the device state abnormality detection result and the device environment abnormality detection result mapping to generate the corrected cashier data source.

[0037] Furthermore, the data correction module 12 is also used for: Retrieving a cashier data compensation record set, and training a cashier data compensation unit that meets a preset compensation accuracy constraint according to the cashier data compensation record set; Based on the cashier data compensation unit, an abnormality compensation analysis is performed on the device state abnormality detection result and the device environment abnormality detection result to obtain initial abnormality compensation data; Sending the initial abnormal compensation data to the cashier machine interaction interface and receiving compensation interaction feedback data; The compensation interaction feedback data and the initial abnormal compensation data are data-fused to generate optimized abnormal compensation data, and the target cashier data source is mapped and corrected according to the optimized abnormal compensation data to obtain the corrected cashier data source.

[0038] Furthermore, the data sorting module 13 is also used for: The multi-side coupling sorting channel includes a pre-processing sorting channel, a compression sorting channel and a sorting verification channel; According to the corrected cashier data source, extracting the e-th corrected cashier data stream, wherein e is a positive integer, e belongs to E, and E is the total number of data streams of the corrected cashier data source; Inputting the e-th corrected cashier data stream into the preprocessing and sorting channel to generate an e-th preprocessed cashier data stream, wherein the preprocessing and sorting channel includes preset cashier data constraint rules; Compressing the e-th pre-processed cashier data stream according to the compression and sorting channel to generate an e-th compressed cashier data stream; Compare and analyze the e-th pre-processed cashier data stream and the e-th compressed cashier data stream according to the sorting and verification channel to generate an e-th compression verification index; Determining whether the e-th compression verification index satisfies the compression verification constraint; If the e-th compression verification index satisfies the compression verification constraint, the e-th compressed cashier data stream is set as the e-th standard cashier data stream, and the e-th standard cashier data stream is added to the standard cashier data source.

[0039] Furthermore, the encryption module 14 is also used for: Perform encryption level identification according to the plurality of standard cashier data streams to generate a plurality of encryption level coefficients; Based on the multiple encryption level coefficients, the multiple standard cashier data streams are assigned encryption schemes according to the encryption modulation model to generate multiple matching encryption schemes; Performing confidence security verification on the multiple matching encryption schemes based on the encrypted modulation record source to obtain multiple verified encryption schemes; The plurality of standard cashier data streams are encrypted based on the plurality of verification encryption schemes to generate the encrypted cashier data source.

[0040] Furthermore, the encryption module 14 is also used for: Traversing the multiple matching encryption schemes and extracting a first matching encryption scheme; Performing a global confidence calculation on the first matching encryption scheme based on the encrypted modulation record source to generate a first global encryption confidence coefficient; Performing local confidence calculation on the first matching encryption scheme based on the encrypted modulation record source to generate a plurality of first local confidence coefficients; Generate a first local encrypted confidence coefficient, wherein the first local encrypted confidence coefficient is obtained by performing mean calculation on the multiple first local confidence coefficients; Determining whether the first global encryption confidence coefficient and the first local encryption confidence coefficient satisfy a bilateral confidence security verification interval; If the first global encryption confidence coefficient and the first local encryption confidence coefficient satisfy the bilateral confidence security verification interval, using the first matching encryption scheme as a first verification encryption scheme; The encrypted modulation recording source is updated according to the first authentication encryption scheme, and the first authentication encryption scheme is added to the plurality of authentication encryption schemes.

[0041] Furthermore, the storage module 15 is also used for: Obtaining multiple individual target capacities corresponding to the multiple encrypted cashier data streams, and calculating a target global capacity according to the multiple individual target capacities; Collecting redundant storage capacity parameters of each cloud storage module in the cloud storage unit to obtain multiple cloud redundant storage capacities, and calculating a global cloud redundant capacity based on the multiple cloud redundant storage capacities; Determining whether the target global capacity is less than the global cloud redundancy capacity; If the target global capacity is less than the global cloud redundant capacity, generating a cloud storage instruction; Based on the cloud storage instruction, the plurality of cloud redundant storage amounts are used as cloud storage constraints, and the plurality of single target capacities are used as cloud storage targets; Perform cloud storage analysis according to the cloud storage constraints and the cloud storage target, determine a cloud storage solution, and store the multiple encrypted cashier data streams in the cloud storage unit according to the cloud storage solution.

[0042] Furthermore, the storage module 15 is also used for: If the target global capacity is greater than / equal to the global cloud redundant capacity, generating an edge storage instruction; According to the edge storage instruction, redundant storage information of each edge storage node in the edge storage unit is collected to obtain multiple edge redundant storage amounts, and a global edge redundant capacity is calculated according to the multiple edge redundant storage amounts; Determining whether the global edge redundancy capacity is greater than / equal to the target global capacity; If the global edge redundant capacity is greater than / equal to the target global capacity, the plurality of edge redundant storage amounts are used as edge storage constraints, and the plurality of monomer target capacities are used as edge storage targets; Perform edge storage allocation according to the edge storage constraint and the edge storage target, and generate multiple initial edge storage solutions; According to the edge node position distribution corresponding to each edge storage node, traverse the multiple initial edge storage schemes to calculate multiple edge storage distances; Based on the multiple edge storage distances, the multiple initial edge storage solutions are screened to obtain a matching edge storage solution, and the multiple encrypted cashier data streams are stored in the edge storage unit according to the matching edge storage solution.

[0043] If the global edge redundancy capacity is less than the target global capacity, an edge optimization instruction is generated, an edge redundancy deviation capacity is calculated according to the global edge redundancy capacity and the target global capacity, and edge node optimization is performed on the edge storage unit according to the edge redundancy deviation capacity.

[0044] The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0045] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A collaborative storage method for cash register data, characterized in that: The method comprises: According to the cashier terminal, a cashier data storage instruction is obtained, wherein the cashier data storage instruction includes a target cashier data source, and the target cashier data source includes a plurality of cashier data streams to be stored; Correcting the target cashier data source based on the error adaptive correction mechanism to obtain a corrected cashier data source, wherein the corrected cashier data source includes a plurality of corrected cashier data streams; The corrected cashier data source is sorted according to the multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes a plurality of standard cashier data streams; Perform linkage encryption modulation on the standard cashier data source according to the encryption modulation model and the encryption modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source includes a plurality of encrypted cashier data streams; Based on the cloud-edge collaborative storage algorithm, the multiple encrypted cashier data streams are stored in a cloud-edge collaborative storage channel, wherein the cloud-edge collaborative storage channel includes a cloud storage unit and an edge storage unit.

2. The method according to claim 1, characterized in that Correcting the target cashier data source based on the error adaptive correction mechanism to obtain a corrected cashier data source includes: Loading source device status monitoring data and source device environment monitoring data corresponding to each cashier data stream to be stored in the target cashier data source; Performing anomaly detection on the source device state monitoring data and the source device environment monitoring data respectively to obtain device state anomaly detection results and device environment anomaly detection results; Performing device abnormality depth evaluation according to the device state abnormality detection result and the device environment abnormality detection result to obtain a device abnormality depth coefficient; Determining whether the device abnormality depth coefficient is less than a device abnormality depth threshold; If the device abnormality depth coefficient is greater than / equal to the device abnormality depth threshold, the target cashier data source is corrected according to the device state abnormality detection result and the device environment abnormality detection result mapping to generate the corrected cashier data source.

3. The method according to claim 2, characterized in that The target cashier data source is corrected according to the device state abnormality detection result and the device environment abnormality detection result, to generate the corrected cashier data source, including: Retrieving a cashier data compensation record set, and training a cashier data compensation unit that meets a preset compensation accuracy constraint according to the cashier data compensation record set; Based on the cashier data compensation unit, an abnormality compensation analysis is performed on the device state abnormality detection result and the device environment abnormality detection result to obtain initial abnormality compensation data; Sending the initial abnormal compensation data to the cashier machine interaction interface and receiving compensation interaction feedback data; The compensation interaction feedback data and the initial abnormal compensation data are data-fused to generate optimized abnormal compensation data, and the target cashier data source is mapped and corrected according to the optimized abnormal compensation data to obtain the corrected cashier data source.

4. The method according to claim 1, characterized in that The correction cashier data source is sorted according to the multilateral coupling sorting channel to generate a standard cashier data source, including: The multi-side coupling sorting channel includes a pre-processing sorting channel, a compression sorting channel and a sorting verification channel; According to the corrected cashier data source, extracting the e-th corrected cashier data stream, wherein e is a positive integer, e belongs to E, and E is the total number of data streams of the corrected cashier data source; Inputting the e-th corrected cashier data stream into the preprocessing and sorting channel to generate an e-th preprocessed cashier data stream, wherein the preprocessing and sorting channel includes preset cashier data constraint rules; Compressing the e-th pre-processed cashier data stream according to the compression and sorting channel to generate an e-th compressed cashier data stream; Compare and analyze the e-th pre-processed cashier data stream and the e-th compressed cashier data stream according to the sorting and verification channel to generate an e-th compression verification index; Determining whether the e-th compression verification index satisfies the compression verification constraint; If the e-th compression verification index satisfies the compression verification constraint, the e-th compressed cashier data stream is set as the e-th standard cashier data stream, and the e-th standard cashier data stream is added to the standard cashier data source.

5. The method according to claim 1, characterized in that The standard cashier data source is linked encrypted and modulated according to the encrypted modulation model and the encrypted modulation record source to obtain the encrypted cashier data source, including: Perform encryption level identification according to the plurality of standard cashier data streams to generate a plurality of encryption level coefficients; Based on the multiple encryption level coefficients, the multiple standard cashier data streams are assigned encryption schemes according to the encryption modulation model to generate multiple matching encryption schemes; Performing confidence security verification on the multiple matching encryption schemes based on the encrypted modulation record source to obtain multiple verified encryption schemes; The plurality of standard cashier data streams are encrypted based on the plurality of verification encryption schemes to generate the encrypted cashier data source.

6. The method according to claim 5, characterized in that The multiple matching encryption schemes are subjected to confidence security verification based on the encrypted modulation record source to obtain multiple verified encryption schemes, including: Traversing the multiple matching encryption schemes and extracting a first matching encryption scheme; Performing a global confidence calculation on the first matching encryption scheme based on the encrypted modulation record source to generate a first global encryption confidence coefficient; Performing local confidence calculation on the first matching encryption scheme based on the encrypted modulation record source to generate a plurality of first local confidence coefficients; Generate a first local encrypted confidence coefficient, wherein the first local encrypted confidence coefficient is obtained by performing mean calculation on the multiple first local confidence coefficients; Determining whether the first global encryption confidence coefficient and the first local encryption confidence coefficient satisfy a bilateral confidence security verification interval; If the first global encryption confidence coefficient and the first local encryption confidence coefficient satisfy the bilateral confidence security verification interval, using the first matching encryption scheme as a first verification encryption scheme; The encrypted modulation recording source is updated according to the first authentication encryption scheme, and the first authentication encryption scheme is added to the plurality of authentication encryption schemes.

7. The method according to claim 1, characterized in that The multiple encrypted cashier data streams are stored in a cloud edge collaborative storage channel based on a cloud edge collaborative storage algorithm, including: Obtaining multiple individual target capacities corresponding to the multiple encrypted cashier data streams, and calculating a target global capacity according to the multiple individual target capacities; Collecting redundant storage capacity parameters of each cloud storage module in the cloud storage unit to obtain multiple cloud redundant storage capacities, and calculating a global cloud redundant capacity based on the multiple cloud redundant storage capacities; Determining whether the target global capacity is less than the global cloud redundancy capacity; If the target global capacity is less than the global cloud redundant capacity, generating a cloud storage instruction; Based on the cloud storage instruction, the plurality of cloud redundant storage amounts are used as cloud storage constraints, and the plurality of single target capacities are used as cloud storage targets; Perform cloud storage analysis according to the cloud storage constraints and the cloud storage target, determine a cloud storage solution, and store the multiple encrypted cashier data streams in the cloud storage unit according to the cloud storage solution.

8. The method according to claim 7, characterized in that Determining whether the target global capacity is less than the global cloud redundancy capacity further includes: If the target global capacity is greater than / equal to the global cloud redundant capacity, generating an edge storage instruction; According to the edge storage instruction, redundant storage information of each edge storage node in the edge storage unit is collected to obtain multiple edge redundant storage amounts, and a global edge redundant capacity is calculated according to the multiple edge redundant storage amounts; Determining whether the global edge redundancy capacity is greater than / equal to the target global capacity; If the global edge redundant capacity is greater than / equal to the target global capacity, the plurality of edge redundant storage amounts are used as edge storage constraints, and the plurality of monomer target capacities are used as edge storage targets; Perform edge storage allocation according to the edge storage constraint and the edge storage target, and generate multiple initial edge storage solutions; According to the edge node position distribution corresponding to each edge storage node, traverse the multiple initial edge storage schemes to calculate multiple edge storage distances; Based on the multiple edge storage distances, the multiple initial edge storage solutions are screened to obtain a matching edge storage solution, and the multiple encrypted cashier data streams are stored in the edge storage unit according to the matching edge storage solution.

9. The method according to claim 8, characterized in that If the global edge redundancy capacity is less than the target global capacity, an edge optimization instruction is generated, an edge redundancy deviation capacity is calculated according to the global edge redundancy capacity and the target global capacity, and edge node optimization is performed on the edge storage unit according to the edge redundancy deviation capacity.

10. A collaborative storage system for cash register data, characterized in that: The system comprises: A storage instruction acquisition module, used for obtaining a cashier data storage instruction according to a cashier terminal, wherein the cashier data storage instruction includes a target cashier data source, and the target cashier data source includes a plurality of cashier data streams to be stored; A data correction module, used for correcting the target cashier data source based on an error adaptive correction mechanism to obtain a corrected cashier data source, wherein the corrected cashier data source includes a plurality of corrected cashier data streams; A data sorting module, used for sorting the corrected cashier data source according to the multilateral coupling sorting channel to generate a standard cashier data source, wherein the standard cashier data source includes a plurality of standard cashier data streams; An encryption module, used for performing linkage encryption modulation on the standard cashier data source according to the encryption modulation model and the encryption modulation record source to obtain an encrypted cashier data source, wherein the encrypted cashier data source includes a plurality of encrypted cashier data streams; A storage module is used to store the multiple encrypted cashier data streams into a cloud-edge collaborative storage channel based on a cloud-edge collaborative storage algorithm, wherein the cloud-edge collaborative storage channel includes a cloud storage unit and an edge storage unit.

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