Data processing method, device, system and electronic device

Abstract

The present application discloses a data processing method, device, system and electronic device. By utilizing multiple computing parties to participate in the data calculation of multiple data providers, and each computing party only obtains the corresponding sub-data blocks of the data to be processed transmitted by multiple data providers instead of the original data, and performs calculation and processing on the multiple sub-data blocks of the multiple data providers, a decentralized computing method based on multi-party secure computing is realized. Based on this computing method, without the need for each data provider to provide real and transparent data to be processed, the data demander can obtain the overall data situation of the data to be processed of multiple data providers based on the calculation results of each computing party (such as the total supply of raw materials corresponding to the existing inventory and production capacity data of each raw material supplier, etc.), which lays a foundation for the accurate production scheduling of data demanders such as core production enterprises. At the same time, the data privacy of multiple data providers is also protected, achieving a win-win situation for all parties.

Description

Technical Field

[0001] The present application belongs to the field of enterprise informatization and information security technology, and in particular, relates to a data processing method, device, system and electronic equipment. Background Art

[0002] When making production scheduling decisions, production (manufacturing) companies need to obtain the raw material supply situation of each upstream raw material supplier in a certain period in the future, and make production scheduling plans based on the integrated data of each supplier.

[0003] Currently, this demand is mostly met by raw material suppliers promising supply quantities to manufacturers. However, since raw material suppliers usually supply multiple manufacturers, they generally do not provide information on their existing inventory, production capacity, and other information that can reflect their maximum available supply to manufacturers for reasons of bargaining power, supply coordination, and data privacy. However, core manufacturers, when market demand is sufficient, usually want to obtain information on the maximum total supply of raw materials and optimize production scheduling based on their own production capacity.

[0004] However, the lack of transparency in supply information between suppliers and manufacturers makes it extremely difficult for core manufacturers to make accurate production plans by combining data from multiple suppliers. Summary of the invention

[0005] To this end, this application discloses the following technical solutions:

[0006] A data processing method, applied to a computing party, comprising:

[0007] Obtaining a corresponding sub-data block of the data to be processed transmitted by each of the multiple data providers; wherein the multiple data providers can be used to respectively provide the required indicator data of the same type on the same production line as the data to be processed, and the indicator data of the same type respectively provided by different data providers are not associated with each other; the data to be processed is data that the data demander does not have access rights to as determined by the data provider based on a one-way access policy;

[0008] According to a first calculation rule, a plurality of sub-data blocks obtained from a plurality of data providers are calculated and processed to obtain a first calculation result;

[0009] The first calculation result is transmitted to the data demander so that the data demander can perform calculations on the multiple first calculation results provided by the multiple calculation parties according to the second calculation rule to perform the required application.

[0010] Optionally, the data to be processed of each data provider includes first data and second data;

[0011] The first data of each data provider corresponds to a plurality of first sub-data blocks, and different first sub-data blocks are respectively transmitted to different computing parties; the second data of each data provider corresponds to a plurality of second sub-data blocks, and different second sub-data blocks are respectively transmitted to different computing parties;

[0012] The multiple first sub-data blocks of the first data and the multiple second sub-data blocks of the second data of each data provider are obtained by the data provider splitting the first data and the second data respectively based on a predetermined splitting rule.

[0013] Optionally, the predetermined splitting rule includes: splitting the data according to random numbers based on the number of computing parties participating in the calculation, so that the number of sub-data blocks obtained by splitting is related to the number of computing parties participating in the calculation, and the value size of the sub-data blocks obtained by splitting is related to the size of the random number.

[0014] Optionally, the step of performing calculation processing on the multiple sub-data blocks obtained from the multiple data providers according to the first calculation rule to obtain a first calculation result includes any one or more of the following:

[0015] Accumulating the multiple first sub-data blocks and the multiple second sub-data blocks obtained from the multiple data providers to obtain a first accumulation result;

[0016] Accumulating the multiple first sub-data blocks obtained from the multiple data providers to obtain a second accumulation result;

[0017] Accumulation processing is performed on the multiple second sub-data blocks obtained from the multiple data providers to obtain a third accumulation result.

[0018] Optionally, transmitting the first calculation result to the data demander includes:

[0019] Any one or more of the first accumulated result, the second accumulated result and the third accumulated result are transmitted to a data demander.

[0020] Optionally, after calculating and processing the multiple sub-data blocks obtained from the multiple data providers, the method further includes:

[0021] Uploading information involved in the process of calculating and processing the multiple sub-data blocks obtained from the multiple data providers to the blockchain for evidence storage;

[0022] Among them, the information uploaded to the blockchain also includes: information involved in the processing process of the data demander calculating and processing multiple first calculation results provided by multiple calculation parties.

[0023] Optionally, the first data is existing inventory data of the data provider, and the second data is production capacity data of the data provider;

[0024] The data demander performs accumulation processing on a plurality of first calculation results provided by a plurality of calculation parties, so as to make a production schedule according to the accumulation processing results.

[0025] A data processing device, applied to a computing party, comprising:

[0026] An acquisition module, used to obtain the corresponding sub-data block of the data to be processed transmitted by each of the multiple data providers; wherein the multiple data providers can be used to respectively provide the necessary indicator data of the same type on the same production line as the data to be processed, and the indicator data of the same type respectively provided by different data providers are not related to each other; the data to be processed is the data that the data demander does not have access rights to, as determined by the data provider based on the one-way access policy;

[0027] A processing module, configured to perform calculation processing on the multiple sub-data blocks obtained from the multiple data providers according to a first calculation rule to obtain a first calculation result;

[0028] The transmission module is used to transmit the first calculation result to the data demander, so that the data demander can perform calculations on multiple first calculation results provided by multiple calculation parties according to the second calculation rule to perform the required application.

[0029] An electronic device, comprising:

[0030] Memory, used to store computer instruction sets;

[0031] A processor is used to implement the data processing method as described in any one of the above items by executing an instruction set stored in a memory.

[0032] A data processing system includes: a plurality of data providers, a plurality of computing parties and a data demander; wherein:

[0033] The data provider is used to transmit different sub-data blocks of the data to be processed to different computing parties;

[0034] The computing party is used to implement corresponding data processing and interaction with the multiple data providers and the data demanders by executing the data processing method as described in any one of the above items;

[0035] The data demander is used to perform calculations on multiple first calculation results provided by multiple calculation parties according to a second calculation rule to perform required applications.

[0036] It can be seen from the above scheme that the data processing method, device, system and electronic device disclosed in the present application, by utilizing multiple computing parties to participate in the data calculation of multiple data providers, and each computing party only obtains the corresponding sub-data blocks of the data to be processed transmitted by multiple data providers instead of the original data, and performs calculation and processing on multiple sub-data blocks of multiple data providers, thereby realizing a decentralized computing method based on multi-party secure computing. Based on this computing method, the data demander can obtain the overall data situation of the data to be processed of multiple data providers based on the calculation results of each computing party without the need for each data provider to provide real and transparent data to be processed (such as the existing inventory and production capacity data of raw material suppliers), such as the total supply of raw materials corresponding to the existing inventory and production capacity data of each raw material supplier, etc., which lays the foundation for the accurate production scheduling of data demanders such as core production enterprises. At the same time, the data privacy of multiple data providers is also protected, achieving a win-win situation for all parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0038] Figure 1 It is a processing flow chart of the data processing method provided by this application;

[0039] Figure 2 is another processing flow chart of the data processing method provided by the present application;

[0040] Figure 3 This is an example diagram of an application based on secure multi-party computing provided by this application;

[0041] Figure 4 It is a structural schematic diagram of a data processing device provided by the present application;

[0042] Figure 5 It is a structural schematic diagram of the electronic device provided by this application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] In order to enable data demanders to obtain the overall data situation of data to be processed of multiple data providers without the need for each data provider to provide real and transparent data to be processed, so as to facilitate the data demanders to execute related applications (such as accurate production scheduling based on the total supply of raw materials corresponding to the existing inventory and production capacity data of each raw material supplier), the embodiments of the present application provide a data processing method, device, system and electronic device.

[0045] The data processing method disclosed in the embodiments of the present application is applied to the computing party, specifically, can be applied to the corresponding electronic device serving as the computing party, which electronic device can be but is not limited to a device under a variety of general or special computing device environments or configurations, such as: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, etc.

[0046] The processing flow of the data processing method provided in the embodiment of the present application is as follows: Figure 1 As shown, specifically including:

[0047] Step 101: Obtain a corresponding sub-data block of to-be-processed data transmitted by each of a plurality of data providers.

[0048] The above-mentioned multiple data providers can be used to respectively provide the necessary indicator data of the same type on the same production line as the data to be processed by the data providers, and the indicator data of the same type provided by different data providers are not related to each other.

[0049] The information of the data provider is one-way access information. The above-mentioned data to be processed is specifically data that the data demander does not have access rights to, as determined by the data provider based on the one-way access policy, that is, the original data of these data cannot be obtained by the data demander.

[0050] A one-way access policy can be a data privacy policy.

[0051] Exemplarily, multiple data providers may be but are not limited to multiple raw material suppliers for related products on a production line (e.g., laptop computers). Multiple raw material suppliers can be used to separately provide their own data on existing inventory, production capacity, etc. of raw materials for the products on the production line. These data are the data to be processed by the raw material suppliers. There is no correlation between the data on existing inventory, production capacity, etc. of raw materials provided by different raw material suppliers.

[0052] Since raw material suppliers usually supply to multiple manufacturers, for reasons of bargaining power, supply coordination, data privacy, etc., raw material suppliers generally do not submit information such as their existing inventory and production capacity that can reflect their maximum available supply to manufacturers. However, when market demand is sufficient, core manufacturers usually hope to cooperate with various suppliers to obtain information on the maximum total supply of raw materials, and optimize production scheduling based on their own production capacity. The lack of transparency in supply information between suppliers and manufacturers makes it extremely difficult for core manufacturers to make accurate production scheduling plans by combining data from multiple suppliers.

[0053] In response to the above problems, the embodiments of the present application propose a decentralized computing method based on multi-party secure computing, based on which data sharing is achieved between data providers and data demanders that can protect the data privacy of data providers.

[0054] Among them, the data demander can be but is not limited to a manufacturing enterprise, and the computing party can be a third party that is different from the raw material supplier and the manufacturing enterprise and can provide secure computing capabilities. Different computing parties are unrelated and cannot communicate data based on agreements or protocols.

[0055] In view of the typical application scenario in which a manufacturing enterprise needs to work with various suppliers to obtain information on the maximum total supply of raw materials and optimize production scheduling based on its own production capacity, this embodiment divides the data to be processed by the data provider into first data and second data, wherein the first data and the second data can be the existing inventory and production capacity data of the raw material supplier in the scenario, respectively, but are not limited to this, and can also be corresponding parameter data in other application scenarios, which can be flexibly set depending on the application scenario.

[0056] With respect to the decentralized computing method based on multi-party secure computing proposed in this application, each of multiple data providers transmits data to multiple computing parties participating in the calculation, and specifically, before transmitting data to the computing party, the data provider pre-splits the data to be processed into multiple sub-data blocks based on the set splitting rules to protect data privacy.

[0057] In the case where the data to be processed by the data provider includes first data and second data, the data provider splits the first data into multiple first sub-data blocks based on a splitting rule, and splits the second data into multiple second sub-data blocks, wherein the sum of the multiple first sub-data blocks is the first data, and the sum of the multiple second sub-data blocks is the second data.

[0058] The following provides an example of the splitting rules used:

[0059] The data is split according to random numbers based on the number of computing parties participating in the calculation, so that the number of sub-data blocks obtained by the splitting is related to the number of computing parties participating in the calculation, and the value size of the sub-data blocks obtained by the splitting is related to the size of the random number.

[0060] For example, assuming that the capacity data of a raw material supplier is 1000 (for example only, not representing the real capacity data), and assuming that the number of computing parties participating in the calculation is 4, the capacity data 1000 needs to be split into 4 sub-data blocks. According to the above splitting rule, a random number of 0-1000 may be first set, such as 370, and based on the random number, 1000 may be split into 370 and 630. Then, a random number of 0-370 may be further set, such as 110, and based on the random number, 370 may be split into 110 and 260. A random number of 0-630 may be set, such as 380, and based on the random number, 630 may be split into 380 and 250. Finally, four random numbers 110, 260, 250 and 380 of the capacity data 1000 are obtained, and the sum of the random numbers is 1000. In this way, the privacy and security of the original capacity data are protected as much as possible, making it difficult to obtain the original capacity data through the reverse deduction process.

[0061] It should be noted that the above splitting rules are not unique. For example, in addition to the above splitting rules combining the number of computing parties and random numbers, splitting rules based on polynomial decomposition can also be used.

[0062] After splitting the data to be processed into multiple sub-data blocks, the data provider distributes the multiple sub-data blocks one-to-one to multiple computing parties participating in the computing.

[0063] Specifically, for example, each data provider distributes multiple first sub-data blocks of the first data one-to-one to multiple computing parties participating in the calculation, and distributes multiple second sub-data blocks of the second data one-to-one to multiple computing parties participating in the calculation. Each computing party participating in the calculation can correspondingly obtain a first sub-data block of the first data and a second sub-data block of the second data, that is, each computing party cannot obtain the complete first data and second data of the data provider.

[0064] To further improve data security, when the data provider transmits a sub-data block of data to be processed to the computing party, preferably, a transmission method based on ciphertext data can be adopted. The data provider can process the sub-data block to be transmitted to the computing party into ciphertext data based on a symmetric key or an asymmetric key, and then transmit it to the computing party.

[0065] Step 102: Perform calculation processing on the multiple sub-data blocks obtained from the multiple data providers according to the first calculation rule to obtain a first calculation result.

[0066] The first calculation rule may depend on the business needs in the specific application scenario. For example, in the application scenario mentioned above where the manufacturing enterprise needs to make an accurate production schedule based on the maximum total raw material supply information of each raw material supplier, the first calculation rule is to perform accumulation / sum operations on each obtained sub-data block. In the shopping settlement scenario, if the total price needs to be calculated based on the unit price and order quantity, the first calculation rule is to perform multiplication operations on the obtained sub-data blocks of each unit price and order quantity.

[0067] Typically, for an application scenario where a manufacturing enterprise needs to perform accurate production scheduling based on the maximum total raw material supply information of each raw material supplier, the step 102 of performing calculation processing based on the first calculation rule may be further implemented to include any one or more of the following:

[0068] 11) performing accumulation processing on the multiple first sub-data blocks and the multiple second sub-data blocks obtained from the multiple data providers to obtain a first accumulation result;

[0069] Taking the example that the first data and the second data are respectively the existing inventory and production capacity data of the raw material supplier, in this implementation, each calculation party participating in the calculation will sum the inventory sub-data blocks (or inventory slices) and production capacity sub-data blocks (or production capacity slices) obtained from multiple raw material suppliers.

[0070] 12) performing accumulation processing on the multiple first sub-data blocks obtained from the multiple data providers to obtain a second accumulation result;

[0071] Continuing with the above example, in this implementation, each computing party participating in the calculation only sums up the inventory sub-data blocks obtained from multiple raw material suppliers.

[0072] 13) Accumulating the multiple second sub-data blocks obtained from the multiple data providers to obtain a third accumulation result.

[0073] Still following the above example, in this implementation, each computing party participating in the calculation only sums up the obtained production capacity sub-data blocks of multiple raw material suppliers.

[0074] Correspondingly, each calculation party performs calculation processing on multiple sub-data blocks obtained from multiple data providers according to the first calculation rule, and obtains a first calculation result, including corresponding one or more of the above-mentioned first cumulative result, second cumulative result and third cumulative result.

[0075] Step 103: Transmit the first calculation result to the data demander, so that the data demander can perform calculations on the multiple first calculation results provided by the multiple calculation parties according to the second calculation rule to perform the required application.

[0076] After obtaining the first calculation result, each calculation party transmits its first calculation result to the data demander. For example, each calculation party transmits one or more of the above-mentioned first cumulative result, second cumulative result and third cumulative result obtained by calculation to the core production enterprise.

[0077] The data demander performs calculations on multiple first calculation results provided by multiple calculation parties according to the second calculation rule as needed, so as to perform the required application according to the calculation results.

[0078] Optionally, the calculation process performed by the data demander based on the second calculation rule may be further implemented as any one or more of the following:

[0079] 21) Performing a summation operation on multiple first accumulation results transmitted by multiple computing parties.

[0080] For example, a manufacturing enterprise further performs summation operations on the accumulated results of the "inventory slice + production capacity slice" of each supplier transmitted by multiple calculation parties, and accordingly obtains the maximum total supply of raw materials from multiple raw material suppliers.

[0081] 22) Performing a summation operation on the multiple second accumulated results transmitted by the multiple computing parties.

[0082] For example, a manufacturing enterprise further performs summation operations on the accumulated inventory slices of each supplier transmitted by multiple computing parties, and accordingly obtains the total existing inventory of multiple raw material suppliers.

[0083] 23) performing a sum operation on the multiple third accumulated results transmitted by the multiple computing parties.

[0084] For example, a manufacturing enterprise further performs summation operations on the accumulated results of the capacity slices of each supplier transmitted by multiple computing parties, and accordingly obtains the total capacity information of multiple raw material suppliers.

[0085] On this basis, data demanders, such as manufacturing companies, can combine the calculation results corresponding to any one or more of the calculation methods in 21)-23) above, such as the maximum total raw material supply, existing total inventory and / or total production capacity information of each raw material supplier, and optimize production scheduling based on their own production capacity.

[0086] For example, a manufacturing company estimates the future total market demand / possible total product volume based on the maximum total raw material supply of each raw material supplier (i.e., the total supply under extreme supply conditions), and based on the future total market demand / possible total product volume and the company's own production capacity, determines whether the current production situation can meet market demand, what production volume can meet market demand, whether it is necessary to expand partners (raw material suppliers), etc.

[0087] It can be seen from the above scheme that the method of this embodiment, by utilizing multiple computing parties to participate in the data calculation of multiple data providers, and each computing party only obtains the corresponding sub-data blocks of the data to be processed transmitted by multiple data providers instead of the original data, and performs calculations and processing on multiple sub-data blocks of multiple data providers, realizes a decentralized computing method based on multi-party secure computing. Based on this computing method, the data demander can obtain the overall data situation of the data to be processed of multiple data providers based on the calculation results of each computing party without the need for each data provider to provide real and transparent data to be processed (such as the existing inventory and production capacity data of raw material suppliers), such as the total supply of raw materials corresponding to the existing inventory and production capacity data of each raw material supplier, etc., which lays the foundation for the accurate production scheduling of data demanders such as core production enterprises. At the same time, the data privacy of multiple data providers is also protected, achieving a win-win situation for all parties.

[0088] In one embodiment, see Figure 2 The data processing method disclosed in the present application further includes the following processing after the computing side performs the computing processing:

[0089] Step 104: Upload the information involved in the process of calculating and processing the multiple sub-data blocks obtained from the multiple data providers to the blockchain for evidence storage.

[0090] In this embodiment, after the calculating party performs the calculation processing according to the first calculation rule, it also uploads the information involved in the processing process of performing the calculation processing according to the first calculation rule to the blockchain for evidence storage, and permanently leaves a trace through the blockchain.

[0091] Among them, uploading the information involved in the processing process of executing the calculation processing according to the first calculation rule to the blockchain for evidence storage may mean: only uploading the calculation result obtained by the calculation party executing the calculation processing according to the first calculation rule to the blockchain for evidence storage, such as uploading the above-mentioned first cumulative result, second cumulative result and / or third cumulative result calculated by the calculation party to the blockchain, etc.

[0092] Alternatively, in another embodiment, the information involved in the processing process of executing the calculation processing according to the first calculation rule is uploaded to the blockchain for evidence storage, which may also mean: in addition to uploading to the blockchain the calculation results obtained by the calculation party executing the calculation processing according to the first calculation rule, the calculation rules adopted by the calculation party (i.e., the first calculation rule), such as summation operations, multiplication operations, etc., are also uploaded.

[0093] In addition, the information uploaded to the blockchain for evidence storage may also include: information involved in the processing process in which the data demander performs calculation processing according to the second calculation rule.

[0094] Similarly, the information involved in the processing process of the data demander performing calculation processing according to the second calculation rule may refer to the calculation results obtained by the data demander performing calculation processing according to the second calculation rule, such as the maximum total supply of raw materials corresponding to multiple raw material suppliers, the existing total inventory and / or total production capacity, etc.; in another embodiment, in addition to the calculation results obtained by the data demander performing calculation processing according to the second calculation rule, the information involved in the processing process of the data demander performing calculation processing according to the second calculation rule may also include the calculation rules adopted by the data demander (i.e., the second calculation rule), such as summation operations, multiplication operations, etc.

[0095] This embodiment ensures that the data is not easily tampered with by permanently recording the information involved in the overall computing process of the computing party and the data demander on the blockchain, realizes audit evidence, and further enhances the security of decentralized computing by combining it with blockchain technology.

[0096] An application example of the present application method is provided below.

[0097] In this example, Figure 3 As shown, it is assumed that there are 3 raw material suppliers: Supplier A, B, and C, and there are 4 computing parties involved in the calculation: Calculation Party 1, 2, 3, 4, and a core production enterprise.

[0098] See also Figure 3 In this example, raw material suppliers, computing parties, and core manufacturing companies can use the following multi-party secure computing process to support core manufacturing companies in making accurate production plans while protecting the data privacy of each raw material supplier:

[0099] 31) Each raw material supplier slices its existing inventory and production capacity data according to the splitting rules, and distributes the sliced ​​data to each computing party.

[0100] like Figure 3 In the example, the existing inventory data x of supplier A is split into x1, x2, x3, x4, and distributed to computing parties 1, 2, 3, and 4; the capacity data a of supplier A is split into a1, a2, a3, a4, and distributed to computing parties 1, 2, 3, and 4. The existing inventory and capacity data of suppliers B and C are split in the same way as supplier A, and the split data slices are distributed to each computing party. Since each computing party obtains the sliced ​​data (sub-data blocks) obtained after the original data (such as inventory and capacity data) is split, no single computing party can summarize the original inventory or capacity data of each supplier.

[0101] 32) Each computing party performs summation processing on the acquired sub-data blocks.

[0102] like Figure 3 As shown, the sum calculated by calculation method 1 is s1=x1+y1+z1+a1+b1+c1, the sum calculated by calculation method 2 is s2=x2+y2+z2+a2+b2+c2, the sum calculated by calculation method 3 is s3=x3+y3+z3+a3+b3+c3, and the sum calculated by calculation method 4 is s4=x4+y4+z4+a4+b4+c4.

[0103] 33) Each calculation party sends its own summed result to the core production enterprise. The core production enterprise obtains the result value of the sum of the received slice data by each calculation party. The core production enterprise then adds the data transmitted by each calculation party to obtain the sum of all slices of each supplier, that is, the maximum total supply of raw materials corresponding to each raw material supplier, but it is impossible to deduce the inventory value or production capacity value of a single supplier.

[0104] like Figure 3 As shown in the figure, the core production enterprise obtains the calculation results S1, S2, S3, and S4 of each calculation party, but does not obtain the initial slice data such as x1, x2, x3, x4, a1, a2, a3, and a4. Therefore, the core production enterprise can calculate the total slice sum of S1+S2+S3+S4, but cannot infer the inventory value or production capacity of any supplier. And the overall calculation process will be permanently recorded through the blockchain, such as Figure 3 The information involved in the overall calculation process of the calculation party and the overall calculation process of the core production enterprise (the calculated result values ​​and calculation rules, etc.) will be stored on the chain to ensure that the data is not easily tampered with and to achieve audit evidence.

[0105] 34) After the core enterprise obtains the sum of the overall inventory and production capacity of each supplier, that is, the maximum total raw material supply corresponding to each raw material supplier, it combines its own production scheduling model to make production plans and achieve production scheduling optimization.

[0106] Corresponding to the above data processing method, the present application also discloses a data processing device, which is applied to a computing side, and the composition structure of the device is as follows: Figure 4 As shown, specifically including:

[0107] The acquisition module 401 is used to obtain the corresponding sub-data block of the to-be-processed data transmitted by each of the multiple data providers; wherein the multiple data providers can be used to respectively provide the necessary indicator data of the same type on the same production line as the to-be-processed data, and the indicator data of the same type respectively provided by different data providers are not related to each other; the to-be-processed data is the data that the data demander does not have access rights to as determined by the data provider based on the one-way access policy;

[0108] The processing module 402 is used to perform calculation processing on the multiple sub-data blocks obtained from the multiple data providers according to the first calculation rule to obtain a first calculation result;

[0109] The transmission module 403 is used to transmit the first calculation result to the data demander, so that the data demander can perform calculations on multiple first calculation results provided by multiple calculation parties according to the second calculation rule to perform the required application.

[0110] In one embodiment, the data to be processed of each data provider includes first data and second data;

[0111] The first data of each data provider corresponds to a plurality of first sub-data blocks, and different first sub-data blocks are respectively transmitted to different computing parties; the second data of each data provider corresponds to a plurality of second sub-data blocks, and different second sub-data blocks are respectively transmitted to different computing parties;

[0112] The multiple first sub-data blocks of the first data and the multiple second sub-data blocks of the second data of each data provider are obtained by the data provider splitting the first data and the second data respectively based on a predetermined splitting rule.

[0113] In one embodiment, the predetermined splitting rules include: splitting the data according to random numbers based on the number of computing parties participating in the calculation, so that the number of sub-data blocks obtained by the splitting is related to the number of computing parties participating in the calculation, and the value size of the sub-data blocks obtained by the splitting is related to the size of the random number.

[0114] In one embodiment, the processing module 402 is specifically used to perform any one or more of the following processing: performing accumulation processing on multiple first sub-data blocks and multiple second sub-data blocks obtained from multiple data providers to obtain a first accumulation result; performing accumulation processing on multiple first sub-data blocks obtained from multiple data providers to obtain a second accumulation result; performing accumulation processing on multiple second sub-data blocks obtained from multiple data providers to obtain a third accumulation result.

[0115] In one implementation, the transmission module 403 is specifically configured to transmit any one or more of the first accumulation result, the second accumulation result, and the third accumulation result to a data demander.

[0116] In one embodiment, the transmission module 403 is further used to: upload information involved in the process of calculating and processing the multiple sub-data blocks obtained from the multiple data providers to the blockchain for evidence storage;

[0117] Among them, the information uploaded to the blockchain also includes: information involved in the processing process of the data demander calculating and processing multiple first calculation results provided by multiple calculation parties.

[0118] For the data processing device disclosed in the embodiment of the present application, since it corresponds to the data processing method disclosed in the above method embodiments, the description is relatively simple. For relevant similarities, please refer to the description of the corresponding method embodiments above, and it will not be described in detail here.

[0119] The present application also discloses an electronic device used as a computing device. The electronic device has a structure as follows: Figure 5 As shown, specifically including:

[0120] Memory 501, used for storing computer instruction sets;

[0121] A set of computer instructions can be implemented in the form of a computer program.

[0122] The processor 502 is used to implement the data processing method disclosed in any of the above method embodiments by executing a computer instruction set.

[0123] The processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices.

[0124] In addition, the electronic device may also include components such as a communication interface and a communication bus. The memory, the processor and the communication interface communicate with each other through the communication bus.

[0125] The communication interface is used for communication between electronic devices and other devices. The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0126] In addition, the embodiment of the present application also discloses a data processing system, including: multiple data providers, multiple computing parties and data demanders; wherein:

[0127] The data provider is used to transmit different sub-data blocks of the data to be processed to different computing parties;

[0128] A computing party, which is used to implement corresponding data processing and interaction with multiple data providers and data demanders by executing the data processing method disclosed in any of the above method embodiments;

[0129] The data demander is used to perform calculations on multiple first calculation results provided by multiple calculation parties according to a second calculation rule to perform the required application.

[0130] The processing performed by the data provider, the calculation party and the data demander and the communication interaction between them can be specifically referred to the description of the above-mentioned method embodiments, which will not be described in detail.

[0131] In summary, the data processing method, device, system, and electronic device disclosed in the embodiments of the present application have the following technical effects compared with the prior art:

[0132] a. Through the decentralized computing method based on multi-party secure computing, the data privacy of data providers is protected and their core interests are guaranteed. Data demanders can use multi-party secure computing to obtain accurate information such as the total supply of raw materials from each data provider, laying the foundation for accurate production scheduling;

[0133] b. By combining blockchain technology, the information involved in the computing process of the computing party and the data demander is stored on the chain, so that the off-chain privacy computing and on-chain storage are organically combined, and the audit storage is realized by utilizing the tamper-proof and easy-to-trace characteristics of the blockchain on-chain data, further enhancing the security of decentralized computing.

[0134] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0135] For the convenience of description, the above system or device is described by dividing it into various modules or units according to its functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0136] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0137] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0138] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method, applied to a computing party, comprising: Obtaining a corresponding sub-data block of the data to be processed transmitted by each of the multiple data providers; wherein the multiple data providers can be used to respectively provide the required indicator data of the same type on the same production line as the data to be processed, and the indicator data of the same type respectively provided by different data providers are not associated with each other; the data to be processed is data that the data demander does not have access rights to as determined by the data provider based on a one-way access policy; According to a first calculation rule, a plurality of sub-data blocks obtained from a plurality of data providers are calculated and processed to obtain a first calculation result; Transmitting the first calculation result to the data demander, so that the data demander can perform calculations on the multiple first calculation results provided by the multiple calculation parties according to the second calculation rule to perform the required application; The data demander performs cumulative processing on multiple first calculation results provided by multiple calculation parties, so as to optimize production scheduling according to the cumulative processing results and its own production capacity; The data demander estimates the total future market demand based on the accumulated processing results, and determines whether the current production situation can meet the market demand based on the total future market demand and its own production capacity; When the current production situation cannot meet market demand, determine the production volume that can meet market demand.

2. The method according to claim 1, wherein the data to be processed by each data provider includes first data and second data; The first data of each data provider corresponds to a plurality of first sub-data blocks, and different first sub-data blocks are respectively transmitted to different computing parties; the second data of each data provider corresponds to a plurality of second sub-data blocks, and different second sub-data blocks are respectively transmitted to different computing parties; The multiple first sub-data blocks of the first data and the multiple second sub-data blocks of the second data of each data provider are obtained by the data provider splitting the first data and the second data respectively based on a predetermined splitting rule.

3. The method according to claim 2, wherein the predetermined splitting rule comprises: The data is split according to random numbers based on the number of computing parties participating in the calculation, so that the number of sub-data blocks obtained by the splitting is related to the number of computing parties participating in the calculation, and the value size of the sub-data blocks obtained by the splitting is related to the size of the random number.

4. The method according to claim 2, wherein the first calculation result obtained by performing calculation processing on the plurality of sub-data blocks obtained from the plurality of data providers according to the first calculation rule includes any one or more of the following: Accumulating the multiple first sub-data blocks and the multiple second sub-data blocks obtained from the multiple data providers to obtain a first accumulation result; Accumulating the multiple first sub-data blocks obtained from the multiple data providers to obtain a second accumulation result; Accumulation processing is performed on the multiple second sub-data blocks obtained from the multiple data providers to obtain a third accumulation result.

5. The method according to claim 4, wherein transmitting the first calculation result to the data demander comprises: Any one or more of the first accumulated result, the second accumulated result and the third accumulated result are transmitted to the data demander.

6. The method according to claim 1, after calculating and processing the multiple sub-data blocks obtained from the multiple data providers, further comprises: Uploading information involved in the process of calculating and processing the multiple sub-data blocks obtained from the multiple data providers to the blockchain for evidence storage; Among them, the information uploaded to the blockchain also includes: information involved in the processing process of the data demander calculating and processing multiple first calculation results provided by multiple calculation parties.

7. According to the method according to any one of claims 2-5, the first data is the existing inventory data of the data provider, and the second data is the production capacity data of the data provider.

8. A data processing device, applied to a computing device, comprising: An acquisition module, used to obtain the corresponding sub-data block of the data to be processed transmitted by each of the multiple data providers; wherein the multiple data providers can be used to respectively provide the necessary indicator data of the same type on the same production line as the data to be processed, and the indicator data of the same type respectively provided by different data providers are not related to each other; the data to be processed is the data that the data demander does not have access rights to, as determined by the data provider based on the one-way access policy; A processing module, configured to perform calculation processing on the multiple sub-data blocks obtained from the multiple data providers according to a first calculation rule to obtain a first calculation result; A transmission module, used for transmitting the first calculation result to a data demander, so that the data demander can perform calculation processing on a plurality of first calculation results provided by a plurality of calculation parties according to a second calculation rule to perform a required application; The data demander performs cumulative processing on multiple first calculation results provided by multiple calculation parties, so as to optimize production scheduling according to the cumulative processing results and its own production capacity; The data demander estimates the total future market demand based on the accumulated processing results, and determines whether the current production situation can meet the market demand based on the total future market demand and its own production capacity; When the current production situation cannot meet market demand, determine the production volume that can meet market demand.

9. An electronic device, comprising: Memory, used to store computer instruction sets; A processor, configured to implement the data processing method according to any one of claims 1 to 7 by executing an instruction set stored in a memory.

10. A data processing system comprising: Multiple data providers, multiple computing parties, and data demanders; among them: The data provider is used to transmit different sub-data blocks of the data to be processed to different computing parties; The computing party is used to implement corresponding data processing and interaction with the multiple data providers and the data demanders by executing the data processing method according to any one of claims 1 to 7; The data demander is used to perform calculations on multiple first calculation results provided by multiple calculation parties according to a second calculation rule to perform required applications.

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