Intelligent agent memory updating method based on multi-source data fusion and conflict resolution

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an agent memory updating method based on multi-source data fusion and conflict resolution, and the method comprises the steps: obtaining information from a plurality of data sources; performing cross validation on the acquired information; then, according to reliability, timeliness and consistency factors of different data sources, a conflict resolution algorithm is determined, weight distribution and final decision making are carried out on conflict information, and a result of the conflict resolution algorithm is determined; and finally, updating the memory data according to the result of the conflict resolution algorithm. Through fusion and conflict resolution of the multi-source data, the accuracy and reliability of the large model agent in the memory data updating process are improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, for example, to an intelligent agent memory update method based on multi-source data fusion and conflict resolution. Background Art

[0002] In a large-model intelligent system, the accuracy of memory data directly affects the decision-making and behavior of the system. However, a single data source may have errors or deviations, resulting in inconsistency between memory data and actual conditions.

[0003] To solve this problem, existing technologies usually adopt a single data source or a simple data fusion method. However, these methods are often unable to effectively resolve conflicts when faced with multi-source data conflicts, resulting in a decrease in the accuracy of the memory data.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application. Summary of the invention

[0005] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0006] The present disclosure provides an agent memory update method based on multi-source data fusion and conflict resolution, the method comprising:

[0007] Obtain information from multiple data sources;

[0008] Cross-validate the information obtained;

[0009] Determine the conflict resolution algorithm based on the reliability, timeliness and consistency factors of different data sources, and make weight allocation and final decision on the conflict information to determine the result of the conflict resolution algorithm;

[0010] Update the memory data based on the results of the conflict resolution algorithm.

[0011] In some embodiments, the conflict resolution algorithm satisfies the formula:

[0012] W i =α·R i +β·T i +γ·C i ,

[0013] Among them, W i represents the weight of the i-th data source; R i represents the reliability of the i-th data source; T i Indicates the timeliness of the i-th data source; Ci represents the consistency of the i-th data source; α, β, γ are the weight coefficients of reliability, timeliness and consistency respectively, and α+β+γ=1.

[0014] In some embodiments, the final decision satisfies the formula:

[0015]

[0016] Where D represents the final decision result; D i Represents the information provided by the i-th data source; n represents the number of data sources.

[0017] In some embodiments, the reliability of the data source R i Satisfy the formula:

[0018]

[0019] Timeliness score of data source T i Satisfy the formula T i =e-λ·Δt, where Δt represents the time difference between the current time and the update time of the data source, and λ is the decay coefficient, which is used to control the decay speed of timeliness;

[0020] Consistency of data sources C i Satisfy the formula:

[0021]

[0022] In some embodiments, based on the reliability, timeliness, and consistency factors of different data sources, a conflict resolution algorithm is determined to assign weights to conflicting information and make a final decision, including:

[0023] Conduct correlation analysis on reliability, timeliness and consistency factors between different data sources;

[0024] Determine the correlation between reliability, timeliness, and consistency factors of data sources;

[0025] Based on the reliability, timeliness and correlation between consistency factors of different data sources, the conflict resolution algorithm is determined, and the weights of conflicting information are allocated and the final decision is made.

[0026] In some embodiments, determining the correlation between reliability, timeliness, and consistency factors of data sources includes:

[0027] Analyze the mutually reinforcing relationship and possible deviation between reliability and consistency, the balanced relationship between reliability and timeliness, and the mutually restrictive relationship between timeliness and consistency.

[0028] The embodiment of the present disclosure provides an agent memory update system based on multi-source data fusion and conflict resolution, the system comprising:

[0029] A multi-source data acquisition module is used to obtain information from multiple data sources;

[0030] A cross-validation module, used to cross-validate the acquired information;

[0031] The conflict resolution module is used to determine the conflict resolution algorithm based on the reliability, timeliness and consistency factors of different data sources, and to perform weight distribution and final decision on the conflict information to determine the result of the conflict resolution algorithm;

[0032] The memory data update module is used to update the memory data according to the result of the conflict resolution algorithm.

[0033] An embodiment of the present disclosure provides an electronic device, the device comprising at least one processor;

[0034] and a memory communicatively coupled to the at least one processor;

[0035] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the above-mentioned intelligent body memory update method based on multi-source data fusion and conflict resolution.

[0036] The embodiment of the present disclosure provides a storage medium storing program instructions, which, when running, execute the above-mentioned intelligent agent memory update method based on multi-source data fusion and conflict resolution.

[0037] The agent memory update method, system, device and storage medium based on multi-source data fusion and conflict resolution provided by the embodiments of the present disclosure can achieve the following technical effects:

[0038] The intelligent agent memory update method, system, device and storage medium based on multi-source data fusion and conflict resolution proposed in the embodiment of the present disclosure first obtains information from multiple data sources; then cross-validates the obtained information; then determines the conflict resolution algorithm based on the reliability, timeliness and consistency factors of different data sources, and performs weight distribution and final decision on the conflict information to determine the result of the conflict resolution algorithm; finally, updates the memory data based on the result of the conflict resolution algorithm. Through the fusion and conflict resolution of multi-source data, the accuracy and reliability of the large model intelligent agent in the memory data update process are improved.

[0039] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0041] Figure 1 It is a flowchart of an intelligent agent memory update method based on multi-source data fusion and conflict resolution provided by an embodiment of the present disclosure;

[0042] Figure 2 It is a flowchart of another agent memory update method based on multi-source data fusion and conflict resolution provided by an embodiment of the present disclosure;

[0043] Figure 3 It is a structural diagram of an intelligent agent memory update system based on multi-source data fusion and conflict resolution provided by an embodiment of the present disclosure;

[0044] Figure 4 It is a structural diagram of an intelligent body memory update device based on multi-source data fusion and conflict resolution provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0045] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0046] The terms "first", "second", etc. in the embodiments of the present disclosure are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so as to describe the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0047] Unless otherwise stated, the term "plurality" means two or more.

[0048] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0049] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0050] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0051] In order to solve the above problems, the present disclosure provides an intelligent agent memory update method, system, device and storage medium based on multi-source data fusion and conflict resolution.

[0052] The following describes the agent memory update method, system, device and storage medium based on multi-source data fusion and conflict resolution provided by the embodiments of the present disclosure in conjunction with the accompanying drawings.

[0053] Figure 1 It is a flow chart of an intelligent agent memory update method based on multi-source data fusion and conflict resolution provided by an embodiment of the present disclosure.

[0054] Combination Figure 1 As shown, the agent memory update method based on multi-source data fusion and conflict resolution may include:

[0055] S101, obtaining information from multiple data sources;

[0056] S102, cross-validating the acquired information;

[0057] S103, determining a conflict resolution algorithm based on the reliability, timeliness and consistency factors of different data sources, and performing weight distribution and final decision on the conflict information to determine the result of the conflict resolution algorithm;

[0058] S104, updating the memory data according to the result of the conflict resolution algorithm.

[0059] In some embodiments, the conflict resolution algorithm satisfies the formula:

[0060] W i =α·R i +β·T i +γ·C i ,

[0061] Among them, W i represents the weight of the i-th data source; R i represents the reliability of the i-th data source; T i Indicates the timeliness of the i-th data source; C i represents the consistency of the i-th data source; α, β, γ are the weight coefficients of reliability, timeliness and consistency respectively, and α+β+γ=1.

[0062] In some embodiments, the final decision satisfies the formula:

[0063]

[0064] Where D represents the final decision result; D i Represents the information provided by the i-th data source; n represents the number of data sources.

[0065] In some embodiments, the reliability of the data source R i Satisfy the formula:

[0066]

[0067] Timeliness score of data source T i Satisfy the formula T i =e-λ·Δt, where Δt represents the time difference between the current time and the update time of the data source, and λ is the decay coefficient, which is used to control the decay speed of timeliness;

[0068] Consistency of data sources C i Satisfy the formula:

[0069]

[0070] In some embodiments, based on the reliability, timeliness, and consistency factors of different data sources, a conflict resolution algorithm is determined to assign weights to conflicting information and make a final decision, including:

[0071] Conduct correlation analysis on reliability, timeliness and consistency factors between different data sources;

[0072] Determine the correlation between reliability, timeliness, and consistency factors of data sources;

[0073] Based on the reliability, timeliness and correlation between consistency factors of different data sources, the conflict resolution algorithm is determined, and the weights of conflicting information are allocated and the final decision is made.

[0074] In some embodiments, determining the correlation between reliability, timeliness, and consistency factors of data sources includes:

[0075] Analyze the mutually reinforcing relationship and possible deviation between reliability and consistency, the balanced relationship between reliability and timeliness, and the mutually restrictive relationship between timeliness and consistency.

[0076] Figure 2 is a flow chart of another agent memory update method based on multi-source data fusion and conflict resolution provided by the embodiment of the present disclosure, combined with Figure 2 ,right Figure 1 The specific technical solution may include:

[0077] 1. Multi-source data fusion mechanism

[0078] Data source acquisition: Obtain information from multiple data sources (such as knowledge bases, online searches, user feedback, etc.).

[0079] Automatic trigger mechanism:

[0080] When a user generates a new memory in the Agent system, the information related to the memory is automatically obtained from multiple data sources.

[0081] When the knowledge base is updated, the multi-source data fusion mechanism is automatically triggered to update the user memory stored in the system.

[0082] Regularly obtain relevant information about the user's existing memory on the Internet to update the user's memory stored in the system.

[0083] Cross-validation: Cross-validate the information obtained to ensure the reliability of the data.

[0084] 2. Conflict Resolution Algorithm

[0085] When the existing memory conflicts with the information obtained from the fusion of multi-source data, the conflict resolution algorithm is triggered.

[0086] The core goal of the conflict resolution algorithm is to resolve the inconsistency problem between multi-source data, thereby providing a reliable basis for memory update. Its working principle can be summarized as follows:

[0087] (1) Data source scoring: Score each data source to assess its reliability, timeliness, and consistency.

[0088] (2) Weight allocation: Based on the scoring results, a weight is assigned to each data source. The higher the weight, the more credible the data source is.

[0089] (3) Decision generation: Generate the final decision result based on the weights and information provided by the data source.

[0090] (4) Memory update: Update the decision results to the agent’s memory system to ensure the accuracy and consistency of the memory data.

[0091] The specific steps are:

[0092] (1) Data source scoring

[0093] The conflict resolution algorithm first scores each data source on three aspects: reliability, timeliness, and consistency.

[0094] Reliability score (R i ):

[0095] The reliability score measures the historical accuracy of a data source. The formula is as follows:

[0096]

[0097] For example, if a data source has provided accurate information 90 out of the past 100 times it has been used, its reliability score is R. i =0.9.

[0098] Timeliness score (T i ):

[0099] The timeliness score measures the timeliness of the information provided by the data source. The formula is as follows:

[0100] T i =e-λ·Δt,

[0101] in:

[0102] Δt represents the time difference between the current time and the data source update time.

[0103] λ is the decay coefficient, which is used to control the decay speed of the timeliness.

[0104] For example, if the information of a data source was updated 1 day ago and λ = 0.1, its timeliness score is T i =e-0.1×1≈0.905.

[0105] Consistency score (C i ):

[0106] The consistency score measures the consistency of a data source with other data sources. The formula is as follows:

[0107]

[0108] For example, if a data source provides 10 pieces of information, 8 of which are consistent with other data sources, its consistency score is C. i =0.8.

[0109] (2) Weight allocation

[0110] Each data source is assigned a weight based on its reliability, timeliness, and consistency scores. The weight calculation formula is as follows:

[0111] W i =α·R i +β·T i +γ·C i ,

[0112] in:

[0113] W i Represents the weight of the i-th data source.

[0114] α, β, γ are the weight coefficients of reliability, timeliness and consistency respectively, and α+β+γ=1.

[0115] The weight coefficient can be adjusted according to the specific application scenario. For example, in a scenario that requires high timeliness, the value of β can be increased.

[0116] (3) Decision making

[0117] The final decision result is generated based on the weights and information provided by the data source. The decision formula is as follows:

[0118]

[0119] in:

[0120] D represents the final decision result.

[0121] D i Represents the information provided by the i-th data source.

[0122] n represents the number of data sources.

[0123] For example, suppose there are three data sources:

[0124] The information provided by data source 1 is D 1 =1, weight is W 1 =0.6.

[0125] The information provided by data source 2 is D 2 =0, weight is W 2 =0.3.

[0126] The information provided by data source 3 is D 3 =1, weight is W 3 =0.1.

[0127] The final decision result is:

[0128] D=0.6×1+0.3×0+0.1×1=0.7,

[0129] If the threshold is set to 0.5, the final decision result is D=1.

[0130] (4) Memory Update

[0131] Update the final decision result to the agent’s memory system. For example, if the final decision result is D = 1, update the relevant information to “true” or “correct”; if D = 0, update the relevant information to “false” or “wrong”.

[0132] In a specific example, combining Figure 1 and Figure 2The agent memory update method based on multi-source data fusion and conflict resolution shown in the figure assumes that the large model agent needs to determine the fact "whether it rains in a certain place today" and obtains information from the following three data sources:

[0133] 1. Knowledge base: Yesterday’s record “It rained in a certain place today”, reliability R1=0.8, timeliness T1=0.7 (the information is outdated), consistency C1=0.6.

[0134] 2. Online search: Real-time weather information shows "no rain in a certain place today", reliability R2 = 0.9, timeliness T2 = 0.95, consistency C2 = 0.8.

[0135] 3. User feedback: User feedback “It is raining in a certain place today”, reliability R3=0.7, timeliness T3=0.9, consistency C3=0.5.

[0136] The weight of each data source is calculated through the conflict resolution algorithm:

[0137] Wa1=0.4×0.8+0.3×0.7+0.3×0.6=0.71

[0138] Wa2=0.4×0.9+0.3×0.95+0.3×0.8=0.885

[0139] Wa3=0.4×0.7+0.3×0.9+0.3×0.5=0.67

[0140] The final decision is:

[0141] D=0.71×1+0.885×0+0.67×1=1.38

[0142] If the threshold is set to 1, the final decision result is "raining" and the memory data is updated.

[0143] The agent memory update method based on multi-source data fusion and conflict resolution proposed in the embodiment of the present disclosure obtains information from multiple data sources; then cross-validates the obtained information; then determines the conflict resolution algorithm based on the reliability, timeliness and consistency factors of different data sources, and performs weight distribution and final decision on the conflict information to determine the result of the conflict resolution algorithm; finally, updates the memory data based on the result of the conflict resolution algorithm. The present disclosure improves the accuracy and reliability of the large model agent (Agent) in the memory data update process through the fusion and conflict resolution of multi-source data.

[0144] and Figure 1 Corresponding to the agent memory update method based on multi-source data fusion and conflict resolution in the present disclosure, the present disclosure also provides an agent memory update system based on multi-source data fusion and conflict resolution, such as Figure 3 As shown, the system may specifically include:

[0145] A multi-source data acquisition module 301 is used to acquire information from multiple data sources;

[0146] A cross-validation module 302, used to cross-validate the acquired information;

[0147] The conflict resolution module 303 is used to determine the conflict resolution algorithm according to the reliability, timeliness and consistency factors of different data sources, and to perform weight distribution and final decision on the conflict information to determine the result of the conflict resolution algorithm;

[0148] The memory data updating module 304 is used to update the memory data according to the result of the conflict resolution algorithm. In some embodiments, the conflict resolution algorithm satisfies the formula:

[0149] W i =α·R i +β·T i +γ·C i ,

[0150] Among them, W i represents the weight of the i-th data source; R i represents the reliability of the i-th data source; T i Indicates the timeliness of the i-th data source; C i represents the consistency of the i-th data source; α, β, γ are the weight coefficients of reliability, timeliness and consistency respectively, and α+β+γ=1.

[0151] In some embodiments, the final decision satisfies the formula:

[0152]

[0153] Where D represents the final decision result; D i Represents the information provided by the i-th data source; n represents the number of data sources.

[0154] Reliability of data source i Satisfy the formula:

[0155]

[0156] Timeliness score of data source T i Satisfy the formula T i =e-λ·Δt, where Δt represents the time difference between the current time and the update time of the data source, and λ is the decay coefficient, which is used to control the decay speed of timeliness;

[0157] Consistency of data sources C i Satisfy the formula:

[0158]

[0159] In some embodiments, based on the reliability, timeliness, and consistency factors of different data sources, a conflict resolution algorithm is determined to assign weights to conflicting information and make a final decision, including:

[0160] Conduct correlation analysis on reliability, timeliness and consistency factors between different data sources;

[0161] Determine the correlation between reliability, timeliness, and consistency factors of data sources;

[0162] Based on the reliability, timeliness and correlation between consistency factors of different data sources, the conflict resolution algorithm is determined, and the weights of conflicting information are allocated and the final decision is made.

[0163] In some embodiments, determining the correlation between reliability, timeliness, and consistency factors of data sources includes:

[0164] Analyze the mutually reinforcing relationship and possible deviation between reliability and consistency, the balanced relationship between reliability and timeliness, and the mutually restrictive relationship between timeliness and consistency.

[0165] Combination Figure 4 As shown, the embodiment of the present disclosure also provides an intelligent body memory update device 400 based on multi-source data fusion and conflict resolution, including a processor 404 and a memory 401. Optionally, the system may also include a communication interface 402 and a bus 403. Among them, the processor 404, the communication interface 402, and the memory 401 can communicate with each other through the bus 403. The communication interface 402 can be used for information transmission. The processor 404 can call the logical instructions in the memory 401 to execute the intelligent body memory update method based on multi-source data fusion and conflict resolution of the above embodiment.

[0166] In addition, the logic instructions in the memory 401 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0167] The memory 401 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 404 executes the function application and data processing by running the program instructions / modules stored in the memory 401, that is, the intelligent agent memory update method based on multi-source data fusion and conflict resolution in the above embodiment is realized.

[0168] The memory 401 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 401 may include a high-speed random access memory and may also include a non-volatile memory.

[0169] The embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as an agent memory update method based on multi-source data fusion and conflict resolution.

[0170] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0171] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0172] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. As used in the description of the embodiments, unless the context clearly indicates otherwise, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or including (comprising) refer to the existence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components and / or these groups. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same or similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0173] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and simplicity of description, the specific working process of the systems, devices and units described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0174] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0175] The flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0176] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0177] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0178] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0179] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0180] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0181] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0182] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0183] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An agent memory update method based on multi-source data fusion and conflict resolution, characterized in that: The method comprises: Obtain information from multiple data sources; Cross-validate the information obtained; Determine the conflict resolution algorithm based on the reliability, timeliness and consistency factors of different data sources, and make weight allocation and final decision on the conflict information to determine the result of the conflict resolution algorithm; Update the memory data based on the results of the conflict resolution algorithm.

2. The method according to claim 1, characterized in that The conflict resolution algorithm satisfies the formula: W i =α·R i +β·T i +γ·C i , Among them, W i represents the weight of the i-th data source; R i represents the reliability of the i-th data source; T i Indicates the timeliness of the i-th data source; C i represents the consistency of the i-th data source; α, β, γ are the weight coefficients of reliability, timeliness and consistency respectively, and α+β+γ=1.

3. The method according to claim 1, characterized in that The final decision satisfies the formula: Where D represents the final decision result; D i Represents the information provided by the i-th data source; n represents the number of data sources.

4. The method according to claim 1, characterized in that: The reliability of the data source R i Satisfy the formula: The timeliness score T of the data source i Satisfy the formula T i =e-λ·Δt, where Δt represents the time difference between the current time and the update time of the data source, and λ is the decay coefficient, which is used to control the decay speed of timeliness; The consistency of the data source C i Satisfy the formula:

5. The method according to claim 1, characterized in that The conflict resolution algorithm is determined based on the reliability, timeliness and consistency factors of different data sources, and the weight distribution and final decision of the conflicting information are performed, including: Conduct correlation analysis on reliability, timeliness and consistency factors between different data sources; Determine the correlation between reliability, timeliness, and consistency factors of data sources; Based on the reliability, timeliness and correlation between consistency factors of different data sources, the conflict resolution algorithm is determined, and the weights of conflicting information are allocated and the final decision is made.

6. The method according to claim 5, characterized in that The determination of the reliability, timeliness and correlation between consistency factors of the data source includes: Analyze the mutually reinforcing relationship and possible deviation between reliability and consistency, the balanced relationship between reliability and timeliness, and the mutually restrictive relationship between timeliness and consistency.

7. An agent memory update system based on multi-source data fusion and conflict resolution, characterized in that: The system comprises: A multi-source data acquisition module is used to obtain information from multiple data sources; A cross-validation module, used to cross-validate the acquired information; The conflict resolution module is used to determine the conflict resolution algorithm based on the reliability, timeliness and consistency factors of different data sources, and to perform weight distribution and final decision on the conflict information to determine the result of the conflict resolution algorithm; The memory data update module is used to update the memory data according to the result of the conflict resolution algorithm.

8. The system according to claim 7, characterized in that The conflict resolution algorithm satisfies the formula: W i =α·R i +β·T i +γ·C i , Among them, W i represents the weight of the i-th data source; R i represents the reliability of the i-th data source; T i Indicates the timeliness of the i-th data source; C i represents the consistency of the i-th data source; α, β, γ are the weight coefficients of reliability, timeliness and consistency respectively, and α+β+γ=1.

9. The system according to claim 7, characterized in that The final decision satisfies the formula: Where D represents the final decision result; D i Represents the information provided by the i-th data source; n represents the number of data sources.

10. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; It is characterized in that the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 6.

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