Enterprise decision support system and method based on big data analysis

Abstract

The invention belongs to the technical field of enterprise management, and particularly relates to an enterprise decision support system and method based on big data analysis. According to the invention, through integration of multi-source data, business dynamics are comprehensively mastered, an accurate decision basis is provided for an enterprise from the perspective of data driving, business changes can be quickly responded through real-time monitoring of business changes, the feasibility of business changes is evaluated in combination with market popularity information, the enterprise is helped to quickly grasp market opportunities or avoid potential risks, and the enterprise quality is improved. Through automation, the time and cost of manual analysis in traditional decision making are reduced, human prejudice is avoided, data-driven objective suggestions are provided, through business cost and market popularity data, an enterprise can more reasonably allocate resources, and the enterprise can quickly adapt to market changes by using a big data analysis technology, so that the enterprise efficiency is improved. And the market coping ability and decision-making efficiency are improved, so that the market competitiveness is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and specifically relates to an enterprise decision support system and method based on big data analysis. Background Art

[0002] With the rapid development of information technology and the widespread application of big data technology, enterprises have accumulated a large amount of multi-source business data in the process of operation, including sales data, inventory data, customer feedback, market trends, etc. These data contain important information about the operation status of the enterprise, market demand, product performance, etc. However, how to extract valuable information from these massive and complex data and transform it into effective decision support is still a major challenge in enterprise management.

[0003] Traditional corporate decision-making methods usually rely on experience and manual analysis, which have problems such as long decision-making cycles, low data processing efficiency, and incomplete decision-making basis. In addition, manual analysis is easily affected by subjective bias, resulting in inaccurate or in-time decisions. This approach is particularly insufficient in a business environment with rapid market changes and fierce competition, and companies are facing increasing decision-making risks and waste of resources.

[0004] In recent years, with the development of technologies such as big data analysis, artificial intelligence and machine learning, enterprise decision support systems have gradually achieved data-driven and automated processing. By cleaning, analyzing and modeling a large amount of enterprise operation data, enterprises can extract key business information from it, and through real-time monitoring and evaluation, generate accurate business change trends and risk assessments, providing more scientific and accurate support for enterprise decision-making. Big data analysis technology can not only greatly improve the efficiency and accuracy of decision-making, but also help enterprises identify potential market opportunities and risks, and improve their competitiveness.

[0005] However, most existing enterprise decision support systems can only focus on data analysis in one aspect (such as market demand, inventory management, etc.), lack comprehensive and integrated analysis of multi-dimensional data, and cannot effectively use multiple data sources to provide accurate decision support for enterprises. Summary of the invention

[0006] The purpose of this invention is to provide an enterprise decision support method based on big data analysis, which can integrate multi-source data and use data mining and analysis technology to enable enterprises to make timely and scientific decisions in a dynamic business environment.

[0007] The technical solution adopted by the present invention is as follows:

[0008] A method for enterprise decision support based on big data analysis, comprising:

[0009] Obtain multi-source business data of the enterprise and obtain key business information based on the multi-source business data;

[0010] Obtain corresponding key business historical data based on key business information, and obtain business change information based on key business historical data and key business information;

[0011] Determine whether the business change information meets the first preset condition. If not, determine that the business has changed and mark it as business change information.

[0012] Obtain corresponding business cost data based on business change information, and obtain business cost information based on business cost data;

[0013] Obtain corresponding business market heat data according to business change information, and obtain corresponding business market heat information according to business market heat data;

[0014] Obtain business risk information based on business change information, business cost information, and business market popularity information, and obtain corresponding decision support information based on the business risk information.

[0015] In a preferred solution, the steps of obtaining multi-source business data of an enterprise and obtaining key business information according to the multi-source business data include:

[0016] Obtain multi-source business data of the enterprise;

[0017] Acquire business information from multiple sources based on multi-source business data;

[0018] Obtain corresponding key feature information based on business information from multiple sources;

[0019] Convert multiple key feature information into business information in a unified format;

[0020] Summarize multiple business information in a unified format and mark the summary results as key business information.

[0021] In a preferred solution, the steps of acquiring corresponding key business historical data according to the key business information, and acquiring business change information according to the key business historical data and the key business information include:

[0022] Obtain corresponding key business historical data based on key business information;

[0023] Acquire corresponding multiple key business history vectors according to key business history data;

[0024] Obtain corresponding key business vectors according to key business information;

[0025] A business change value is obtained according to a plurality of key business history vectors and a key business vector, and is marked as business change information.

[0026] In a preferred solution, the step of determining whether the service change information meets the first preset condition, and if not, determining that the service has changed and marking it as service change information, includes:

[0027] Get the business change assessment interval value;

[0028] Determine whether the business change information is within the business change assessment interval;

[0029] If the service change information is within the service change assessment interval, the service change is determined to be stable;

[0030] If the service change information is not within the service change assessment interval value and exceeds the upper limit of the service change assessment interval value, the service change is determined to be incremental and marked as service change information;

[0031] If the service change information is not within the service change evaluation interval value and is lower than the lower limit of the service change evaluation interval value, the service change is determined to be decreasing and marked as service change information.

[0032] In a preferred solution, the step of obtaining corresponding business cost data according to the business change information and obtaining business cost information according to the business cost data includes:

[0033] Obtain corresponding business cost data based on business change information;

[0034] Acquire multiple corresponding business cost values ​​according to the business cost data;

[0035] Get standard business cost value;

[0036] Obtain enterprise operation weight;

[0037] The business cost weight is obtained according to a plurality of business cost values, enterprise operation weights, and standard business cost values, and is marked as business cost information.

[0038] In a preferred embodiment, the step of obtaining the enterprise operation weight includes:

[0039] Obtaining information about business operations;

[0040] Obtain business operation strategies corresponding to key business information based on enterprise operation information;

[0041] Obtain the corresponding enterprise operation vector according to the enterprise operation strategy;

[0042] Obtaining an operation table, wherein the operation table includes a plurality of enterprise operation vectors and an enterprise operation weight corresponding to each enterprise operation vector;

[0043] According to the enterprise operation vector, the corresponding enterprise operation weight is obtained from the operation table.

[0044] In a preferred solution, the step of obtaining corresponding business market heat data according to the business change information and obtaining corresponding business market heat information according to the business market heat data includes:

[0045] Obtain the corresponding business market popularity data based on business change information;

[0046] Obtain the corresponding business market heat matrix according to the business market heat data;

[0047] Obtain multiple elements in the business market heat matrix;

[0048] The business market heat weight is obtained according to multiple elements and marked as business market heat information.

[0049] In a preferred solution, the steps of obtaining business risk information according to business change information, business cost information, and business market popularity information, and obtaining corresponding decision support information according to the business risk information include:

[0050] Obtain business risk information based on business change information, business cost information, and business market popularity information;

[0051] Obtaining a decision evaluation table, wherein the decision evaluation table includes a plurality of business risk interval values ​​and decision support information corresponding to each business risk interval value;

[0052] Obtain the corresponding target business risk interval value according to the business risk information;

[0053] Obtain corresponding decision support information from the decision evaluation table based on the target business risk interval value.

[0054] The present invention also provides an enterprise decision support system based on big data analysis, which is used for the enterprise decision support method based on big data analysis, comprising:

[0055] Key business module, used to obtain multi-source business data of the enterprise and obtain key business information based on multi-source business data;

[0056] The business change module is used to obtain corresponding key business historical data according to key business information, and obtain business change information according to key business historical data and key business information;

[0057] The service change module is used to determine whether the service change information meets the first preset condition. If not, it is determined that the service has changed and marked as service change information;

[0058] The business cost module is used to obtain the corresponding business cost data according to the business change information, and obtain the business cost information according to the business cost data;

[0059] The market heat module is used to obtain the corresponding business market heat data according to the business change information, and obtain the corresponding business market heat information according to the business market heat data;

[0060] The decision support module is used to obtain business risk information based on business change information, business cost information and business market popularity information, and obtain corresponding decision support information based on the business risk information.

[0061] And, an enterprise decision support terminal based on big data analysis, comprising:

[0062] one or more processors;

[0063] a storage device having one or more programs stored thereon;

[0064] When one or more programs are executed by one or more processors, the one or more processors implement an enterprise decision support method based on big data analysis.

[0065] The technical effects achieved by the present invention are:

[0066] The present invention, through multi-source data integration, comprehensively grasps business dynamics, provides enterprises with accurate decision-making basis from a data-driven perspective, can quickly respond to business changes by real-time monitoring of business changes, and evaluate the feasibility of business changes in combination with market popularity information, helping enterprises to quickly seize market opportunities or avoid potential risks. It reduces the time and cost of manual analysis in traditional decision-making through automation, avoids human bias, and provides data-driven objective suggestions. Through business cost and market popularity data, enterprises can allocate resources more reasonably and ensure that business priorities match market demand, thereby optimizing strategic planning. Using big data analysis technology, enterprises can quickly adapt to market changes, improve market response capabilities and decision-making efficiency, thereby enhancing market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of the method provided by the present invention;

[0068] Figure 2 It is a system module diagram provided by the present invention. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0070] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0071] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0072] Secondly, the present invention is described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the schematic diagrams are only examples and should not limit the scope of protection of the present invention.

[0073] Please refer to the attached Figure 1 As shown, a method for enterprise decision support based on big data analysis is provided, including:

[0074] S1. Obtain multi-source business data of the enterprise and obtain key business information based on the multi-source business data;

[0075] S2. Obtain corresponding key business historical data based on the key business information, and obtain business change information based on the key business historical data and the key business information;

[0076] S3, judging whether the service change information meets the first preset condition, if not, judging that the service has changed, and marking it as service change information;

[0077] S4. Obtain corresponding business cost data according to the business change information, and obtain business cost information according to the business cost data;

[0078] S5. Obtain corresponding business market heat data according to the business change information, and obtain corresponding business market heat information according to the business market heat data;

[0079] S6. Obtain business risk information based on business change information, business cost information, and business market popularity information, and obtain corresponding decision support information based on the business risk information.

[0080] As in the above steps S1 to S6, key information is extracted from the multi-source business data of the enterprise, such as sales data, inventory information, customer feedback, etc., redundant data is removed and key business information is extracted through data cleaning, and the key business information is compared with historical data to obtain business change information, such as sales increase and decrease trends, inventory change trends, etc., so as to grasp the dynamic changes of the business, and the business change information is compared with the first preset condition (such as growth rate threshold, inventory change range, etc.). If the change exceeds the preset condition range, it is marked as business change information. According to the business change information, the cost information of the relevant business is obtained through data matching and cost analysis models, such as new product development costs, marketing costs, etc., to form a cost assessment of the business change, and the business change information is combined with market popularity data (such as search index, social media discussion, industry reports, etc.) to evaluate the market demand for the business, and comprehensively analyze the business change information. It can obtain business risk information of business changes through information, cost information and market heat information, and provide support information for decision-making, such as whether it is necessary to adjust production plans and optimize supply chains. Through multi-source data integration, it can fully grasp business dynamics and provide enterprises with accurate decision-making basis from a data-driven perspective. By real-time monitoring of business changes, it can quickly respond to business changes and evaluate the feasibility of business changes in combination with market heat information, helping enterprises to quickly seize market opportunities or avoid potential risks. Through automation, it reduces the time and cost of manual analysis in traditional decision-making, avoids human bias, and provides data-driven objective suggestions. Through business cost and market heat data, enterprises can allocate resources more reasonably and ensure that business priorities match market demand, thereby optimizing strategic planning. Using big data analysis technology, enterprises can quickly adapt to market changes, improve market response capabilities and decision-making efficiency, thereby enhancing market competitiveness.

[0081] In a preferred embodiment, the step of obtaining multi-source business data of an enterprise and obtaining key business information according to the multi-source business data includes:

[0082] S101. Acquire multi-source business data of the enterprise;

[0083] S102, acquiring multiple source business information according to multi-source business data;

[0084] S103, obtaining corresponding key feature information according to multiple source business information;

[0085] S104, converting the plurality of key feature information into business information in a unified format;

[0086] S105: Aggregate multiple business information in a unified format, and mark the aggregation result as key business information.

[0087] As in the above steps S101 to S105, business data is collected from multiple data sources (such as ERP system, CRM system, financial system, market research report, social media data, etc.), covering key business information inside and outside the enterprise, ensuring the comprehensiveness of data sources, extracting corresponding business information from multi-source business data, such as inventory records, sales data, customer feedback, etc., using data extraction technology, such as API interface, database query, crawler technology, etc., to ensure efficient acquisition of data from different sources, extracting features of business information from multiple sources, identifying key information (such as sales, growth rate, inventory turnover rate, etc.), filtering irrelevant or redundant information through data mining technology, and extracting feature data that is of great significance to enterprise decision-making. Since the formats and structures of different data sources may be inconsistent (such as time format, unit, data field name), data standardization technology is used to convert multiple key feature information into a unified data format and unit, and multiple business information in a unified format are integrated and summarized to generate comprehensive key business information, which is marked as key business information. By integrating multi-source data from inside and outside the enterprise, all-round business information of the enterprise is covered, helping decision-makers understand the current status of enterprise operations from multiple perspectives.

[0088] In a preferred embodiment, the steps of acquiring corresponding key business historical data according to the key business information, and acquiring business change information according to the key business historical data and the key business information include:

[0089] S201. Obtain corresponding key business historical data according to key business information;

[0090] S202, acquiring corresponding multiple key business history vectors according to the key business history data;

[0091] S203, acquiring a corresponding key business vector according to the key business information;

[0092] S204: Obtain a business change value according to a plurality of key business history vectors and a key business vector, and mark it as business change information.

[0093] As in the above steps S201 to S204, according to the current key business information, the relevant records in the historical database are matched. For example, if the key business information includes the sales data of a certain product, the sales records of the product are extracted from the historical database. The time range can be flexibly set according to the needs (such as the past 1 year or a specific period). The extracted key business historical data is vectorized and converted into a multi-dimensional vector through standardization and normalization. Each dimension represents a specific key business indicator (such as sales volume, market share, etc. at a point in time), thereby forming multiple key business historical vectors. The current key business information is subjected to the same vectorization process to generate a key business vector for direct comparison and analysis with the historical vector. The business change value is calculated using the historical vector and the current vector, which is marked as business change information. The calculation formula of the business change value is: Where Y change It is represented by the business change value, i represents the number of multiple key business history vectors, i = 1, 2, 3...n, (Y history ) i Represented as the i-th key business history vector, Y now Represented as key business vectors, it can accurately capture business changes in different time periods, avoid subjective misjudgments that may exist in traditional manual analysis, make full use of the company's historical data assets, improve their operability through vectorized processing, and maximize the support role of historical data for corporate decision-making.

[0094] In a preferred implementation, the step of determining whether the service change information meets the first preset condition, and if not, determining that the service has changed and marking it as service change information, includes:

[0095] S301, obtaining a service change assessment interval value;

[0096] S302, determining whether the service change information is within the service change assessment interval value;

[0097] If the service change information is within the service change assessment interval, the service change is determined to be stable;

[0098] If the service change information is not within the service change assessment interval value and exceeds the upper limit of the service change assessment interval value, the service change is determined to be incremental and marked as service change information;

[0099] If the service change information is not within the service change evaluation interval value and is lower than the lower limit of the service change evaluation interval value, the service change is determined to be decreasing and marked as service change information.

[0100] As in the above steps S301 to S302, a business change evaluation interval value is generated according to the business needs of the enterprise and the statistical results of historical data. The evaluation interval value consists of an upper and lower limit, indicating a reasonable fluctuation range of business changes, which is usually determined by business rules, industry standards or data analysis methods (such as mean plus or minus standard deviation). The business change information is compared with the evaluation interval value to determine whether the information is between the upper and lower limits. This process is achieved through simple numerical comparison to quickly determine the change situation. If the business change information is within the evaluation interval value, it means that the business change is within a reasonable range and the fluctuation is small. No additional processing is required and it is determined to be "stable". The analysis results are recorded but no further operation is performed. If the business change information exceeds the upper limit of the evaluation interval value, it indicates that the business is growing at a rate exceeding expectations. The change is marked as incremental business change information for decision makers to make positive adjustments, such as increasing resource investment or expanding the market. If the business change information is lower than the lower limit of the evaluation interval value, it means that the business has an unexpected decrease trend. The change is marked as decreasing business change information so that the enterprise can take remedial measures, such as optimizing operating strategies or adjusting product lines. It can quickly identify abnormal situations in business changes and improve analysis efficiency.

[0101] In a preferred embodiment, the step of obtaining corresponding business cost data according to the business change information and obtaining business cost information according to the business cost data includes:

[0102] S401. Obtain corresponding business cost data according to business change information;

[0103] S402, acquiring corresponding multiple business cost values ​​according to the business cost data;

[0104] S403, obtaining a standard business cost value;

[0105] S404, obtaining the enterprise operation weight;

[0106] S405 . Obtain a business cost weight according to a plurality of business cost values, an enterprise operation weight, and a standard business cost value, and mark it as business cost information.

[0107] As in the above steps S401 to S405, the corresponding business cost data is extracted from the relevant database according to the marked business change information. These data may include raw material costs, production costs, transportation costs, labor costs, etc. The specific content depends on the business type. The extracted business cost data is disassembled or classified to form multiple business cost values. For example, the total cost is decomposed into cost values ​​of different links or departments. The standard business cost value is usually a reference value set by the enterprise based on historical data, industry standards or specific operating goals. This standard value is obtained from the historical database or preset parameters as a benchmark for evaluating actual business costs. The enterprise operation weight is used to adjust the importance coefficient of business costs in different scenarios. The weight is dynamically adjusted according to the current operating status and strategy. The business cost weight is calculated by combining multiple business cost values, enterprise operation weights and standard business cost values, and the calculation result is marked as business cost information. The calculation formula for the business cost weight is: Where Q represents the business cost weight, k represents the number of multiple business cost values, k = 1, 2, 3...p, (Ccost) k Expressed as the kth business cost value, C standard It is represented as the standard business cost value, and E is represented as the enterprise operation weight, which provides a multi-dimensional cost analysis perspective, enabling enterprises to accurately understand the cost structure and its changing trends in different links, and dynamically adjust the cost analysis results to reflect the actual needs of the current business environment and operation strategies.

[0108] In a preferred embodiment, the step of obtaining the enterprise operation weight includes:

[0109] S4041. Obtain enterprise operation information;

[0110] S4042. Obtaining business operation strategies corresponding to key business information based on enterprise operation information;

[0111] S4043. Obtaining a corresponding enterprise operation vector according to the enterprise operation strategy;

[0112] S4044. Obtain an operation table, where the operation table includes a plurality of enterprise operation vectors and an enterprise operation weight corresponding to each enterprise operation vector;

[0113] S4045. Obtain corresponding enterprise operation weights from the operation table according to the enterprise operation vector.

[0114] As in the above steps S4041 to S4045, current operation information, such as production plans, inventory levels, market sales data, financial statements, etc., is extracted from the enterprise operation database as the basic data source for analyzing the enterprise operation status. According to the key business information, the relevant business operation strategies are matched, such as production acceleration plans, market expansion strategies or cost saving measures. These strategies may be generated by the enterprise's operation planning documents, historical records or AI recommendations. The key indicators in the enterprise operation strategy (such as production capacity targets, inventory adjustments, market coverage) are quantified and converted into a multidimensional vector (enterprise operation vector) in a unified format. The operation table is generated by historical data, simulation analysis or expert evaluation, and contains multiple operation vectors and their corresponding weights. For example, vectors under different market conditions may correspond to different importance weights. According to the generated enterprise operation vector, the closest match is found in the operation table, and the corresponding enterprise operation weight is extracted, which can accurately identify the specific impact of the operation strategy on the business and help enterprises better understand and optimize operation planning.

[0115] In a preferred embodiment, the step of obtaining corresponding business market heat data according to the business change information and obtaining corresponding business market heat information according to the business market heat data includes:

[0116] S501. Obtain corresponding business market popularity data according to business change information;

[0117] S502, acquiring a corresponding business market heat matrix according to the business market heat data;

[0118] S503, obtaining multiple elements in the business market heat matrix;

[0119] S504: Obtain business market heat weight according to multiple elements, and mark it as business market heat information.

[0120] As in the above steps S501 to S504, according to the business change information, the market data source related to the business (such as search trends, sales data, social media activity, etc.) is called to extract the original data reflecting the market heat. For example, for a certain product, its search engine keyword traffic, number of online comments, sales growth rate and other data will be obtained. The extracted market heat data is cleaned and processed to form a multi-dimensional business market heat matrix. The rows of the matrix may represent different market dimensions (such as region, time period, channel), and the columns may represent specific market heat indicators (such as click-through rate, number of interactions). It can also be a single-row matrix. Each element in the single-row matrix represents an indicator. The matrix form structures the data. Multiple matrix elements related to key businesses are extracted from the heat matrix. For example, market performance data within a specific time period can be selected, or the heat indicators of a specific area can be focused on. The business market heat weight is weighted to calculate the business market heat weight and marked as business market heat information. The calculation formula for the business market heat weight is: In the formula, R weight It is represented by the business market heat weight, t is the number of multiple elements, t = 1, 2, 3...g, W t Represented as the tth element, it can intuitively reflect the market’s attention to the business and provide accurate data support for enterprises to understand market dynamics.

[0121] In a preferred embodiment, the steps of obtaining business risk information according to business change information, business cost information and business market popularity information, and obtaining corresponding decision support information according to the business risk information include:

[0122] S601. Obtain business risk information based on business change information, business cost information, and business market popularity information;

[0123] S602: Obtain a decision evaluation table, wherein the decision evaluation table includes a plurality of business risk interval values ​​and decision support information corresponding to each business risk interval value;

[0124] S603, obtaining a corresponding target business risk interval value according to the business risk information;

[0125] S604. Obtain corresponding decision support information from the decision evaluation table according to the target business risk interval value.

[0126] As in the above steps S601 to S604, the business change information, business cost information and business market popularity information are comprehensively analyzed to generate a specific business risk value and mark it as business risk information. The calculation formula of business risk information is F=Y change Q R weight , where F represents business risk information, Y changeIt is represented by the business change value, Q is represented by the business cost weight, and R weight It is expressed as the weight of business market heat. The decision evaluation table is constructed through expert experience or historical data, and contains multiple business risk interval values ​​(such as low risk, medium risk, high risk) and corresponding decision support information (such as continuing to invest, adjusting strategies, suspending business, etc.). Each interval value is expressed in a specific risk value range and is accompanied by corresponding recommended actions. According to the generated business risk information, the corresponding risk interval value in the decision evaluation table is found. For example, if the business risk information is 8.5 and the risk interval is [8-9], indicating high risk, then it is determined that the business risk is in the high risk interval. According to the target business risk interval value, the corresponding decision support information is extracted from the decision evaluation table, such as "optimizing operating strategies", "increasing marketing promotion budgets" or "reducing business expansion", etc. The extracted decision support information will be used as the final output to provide guidance for corporate decision-making, accurately assess the risks faced by the business, help the company to fully grasp the risk status, adapt to the ever-changing business environment, and provide optimized solutions for the company in different scenarios.

[0127] Please refer to the attached Figure 2 As shown, the present invention also provides an enterprise decision support system based on big data analysis, which is used for the above-mentioned enterprise decision support method based on big data analysis, including:

[0128] Key business module, used to obtain multi-source business data of the enterprise and obtain key business information based on multi-source business data;

[0129] The business change module is used to obtain corresponding key business historical data according to key business information, and obtain business change information according to key business historical data and key business information;

[0130] The service change module is used to determine whether the service change information meets the first preset condition. If not, it is determined that the service has changed and marked as service change information;

[0131] The business cost module is used to obtain the corresponding business cost data according to the business change information, and obtain the business cost information according to the business cost data;

[0132] The market heat module is used to obtain the corresponding business market heat data according to the business change information, and obtain the corresponding business market heat information according to the business market heat data;

[0133] The decision support module is used to obtain business risk information based on business change information, business cost information and business market popularity information, and obtain corresponding decision support information based on the business risk information.

[0134] As mentioned above, the key business module extracts key business information from the enterprise's multi-source business data (such as sales, inventory, market feedback, etc.), and generates unified key business information by cleaning, format conversion and feature extraction of data from multiple sources. The business change module generates business change information by comparing current key business information with historical key business data, and identifies business development trends or abnormal changes. The business change module evaluates whether the business change information meets the enterprise's preset change range conditions, and marks the situation that exceeds the range as business change information. The business cost module calculates the cost changes caused by business changes and generates comprehensive business cost information, including operating costs, resource allocation costs, etc. The market heat module obtains the impact of the market heat of the business and generates business market heat information that reflects market attention. The decision support module comprehensively evaluates business risks based on business change information, cost information and market heat information, and generates corresponding decision support information by matching decision evaluation tables. By integrating multi-source business data, the system can comprehensively and dynamically reflect the enterprise's operating status and effectively avoid the limitations of a single data perspective.

[0135] And, an enterprise decision support terminal based on big data analysis, comprising:

[0136] one or more processors;

[0137] a storage device having one or more programs stored thereon;

[0138] When one or more programs are executed by one or more processors, the one or more processors implement an enterprise decision support method based on big data analysis.

[0139] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. An enterprise decision support method based on big data analysis, characterized in that: include: Obtain multi-source business data of the enterprise and obtain key business information based on the multi-source business data; Obtain corresponding key business historical data based on key business information, and obtain business change information based on key business historical data and key business information; Determine whether the business change information meets the first preset condition. If not, determine that the business has changed and mark it as business change information. Obtain corresponding business cost data based on business change information, and obtain business cost information based on business cost data; Obtain corresponding business market heat data according to business change information, and obtain corresponding business market heat information according to business market heat data; Obtain business risk information based on business change information, business cost information, and business market popularity information, and obtain corresponding decision support information based on the business risk information.

2. The enterprise decision support method based on big data analysis according to claim 1 is characterized in that: The steps of obtaining multi-source business data of an enterprise and obtaining key business information based on the multi-source business data include: Obtain multi-source business data of the enterprise; Acquire business information from multiple sources based on multi-source business data; Obtain corresponding key feature information based on business information from multiple sources; Convert multiple key feature information into business information in a unified format; Summarize multiple business information in a unified format and mark the summary results as key business information.

3. The enterprise decision support method based on big data analysis according to claim 1 is characterized in that: The steps of obtaining corresponding key business historical data according to the key business information and obtaining business change information according to the key business historical data and the key business information include: Obtain corresponding key business historical data based on key business information; Acquire corresponding multiple key business history vectors according to key business history data; Obtain corresponding key business vectors according to key business information; A business change value is obtained according to a plurality of key business history vectors and a key business vector, and is marked as business change information.

4. The enterprise decision support method based on big data analysis according to claim 1 is characterized in that: The step of judging whether the service change information meets the first preset condition, and if not, judging that the service has changed and marking it as service change information includes: Get the business change assessment interval value; Determine whether the business change information is within the business change assessment interval; If the service change information is within the service change assessment interval, the service change is determined to be stable; If the service change information is not within the service change assessment interval value and exceeds the upper limit of the service change assessment interval value, the service change is determined to be incremental and marked as service change information; If the service change information is not within the service change evaluation interval value and is lower than the lower limit of the service change evaluation interval value, the service change is determined to be decreasing and marked as service change information.

5. The enterprise decision support method based on big data analysis according to claim 1 is characterized in that: The steps of obtaining corresponding business cost data according to the business change information and obtaining business cost information according to the business cost data include: Obtain corresponding business cost data based on business change information; Acquire multiple corresponding business cost values ​​according to the business cost data; Get standard business cost value; Obtain enterprise operation weight; The business cost weight is obtained according to a plurality of business cost values, enterprise operation weights, and standard business cost values, and is marked as business cost information.

6. The enterprise decision support method based on big data analysis according to claim 1 is characterized in that: The steps to obtain the enterprise operation weight include: Obtaining information about business operations; Obtain business operation strategies corresponding to key business information based on enterprise operation information; Obtain the corresponding enterprise operation vector according to the enterprise operation strategy; Obtaining an operation table, wherein the operation table includes a plurality of enterprise operation vectors and an enterprise operation weight corresponding to each enterprise operation vector; According to the enterprise operation vector, the corresponding enterprise operation weight is obtained from the operation table.

7. The enterprise decision support method based on big data analysis according to claim 1 is characterized in that: The step of obtaining corresponding business market heat data according to the business change information and obtaining corresponding business market heat information according to the business market heat data includes: Obtain corresponding business market popularity data based on business change information; Obtain the corresponding business market heat matrix according to the business market heat data; Obtain multiple elements in the business market heat matrix; The business market heat weight is obtained according to multiple elements and marked as business market heat information.

8. The enterprise decision support method based on big data analysis according to claim 1 is characterized in that: The steps of obtaining business risk information based on business change information, business cost information, and business market popularity information, and obtaining corresponding decision support information based on the business risk information include: Obtain business risk information based on business change information, business cost information, and business market popularity information; Obtaining a decision evaluation table, wherein the decision evaluation table includes a plurality of business risk interval values ​​and decision support information corresponding to each business risk interval value; Obtain the corresponding target business risk interval value according to the business risk information; Obtain corresponding decision support information from the decision evaluation table based on the target business risk interval value.

9. An enterprise decision support system based on big data analysis, applied to the enterprise decision support method based on big data analysis as claimed in any one of claims 1 to 8, characterized in that: include: Key business module, used to obtain multi-source business data of the enterprise and obtain key business information based on multi-source business data; The business change module is used to obtain corresponding key business historical data according to key business information, and obtain business change information according to key business historical data and key business information; The service change module is used to determine whether the service change information meets the first preset condition. If not, it is determined that the service has changed and marked as service change information; The business cost module is used to obtain the corresponding business cost data according to the business change information, and obtain the business cost information according to the business cost data; The market heat module is used to obtain the corresponding business market heat data according to the business change information, and obtain the corresponding business market heat information according to the business market heat data; The decision support module is used to obtain business risk information based on business change information, business cost information and business market popularity information, and obtain corresponding decision support information based on the business risk information.

10. An enterprise decision support terminal based on big data analysis, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When one or more programs are executed by one or more processors, the one or more processors implement the enterprise decision support method based on big data analysis as described in any one of claims 1 to 8.

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