Environmental adjustment parameter determination method and device of service resources, equipment and medium

Abstract

The invention relates to a method and device for determining environment adjustment parameters of service resources, equipment and a medium. The method comprises the following steps: acquiring use environment data and user evaluation data of service resources; determining environment adjustment parameters of the service resources according to the use environment data; determining user influence parameters of the service resources according to the user evaluation data; and adjusting the environment adjustment parameter according to the user influence parameter so as to update the environment adjustment parameter of the service resource. By adopting the method, the environmental parameter adjustment and the environmental tightness can be realized.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method, apparatus, equipment and medium for determining environmental adjustment parameters of service resources. Background Technology

[0002] With the development of big data technology, automated environmental adjustment parameter determination technology based on data-driven and intelligent algorithms has emerged. This technology can analyze massive amounts of data to build predictive models, enabling rapid and accurate estimation of financial product prices. Its key feature is its ability to uncover complex nonlinear relationships and reduce reliance on subjective human experience.

[0003] Traditional methods primarily rely on human experience and comparison with historical data. Currently, the main methods for determining environmental adjustment parameters for service resources in the market include the comparable company method, the yield curve spread method, the interest rate term structure model, and the book-building pricing method.

[0004] However, traditional methods have long data processing and decision-making cycles, making it difficult to quickly capture and quantify the impact of unstructured information such as market sentiment and breaking news during the critical window of service resource issuance, leading to a disconnect between environmental adjustment parameters and environmental conditions. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, equipment, and medium for determining environmental adjustment parameters of service resources that can improve the closeness between environmental adjustment parameters and the environment, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for determining environmental adjustment parameters of service resources, including:

[0007] Obtain usage environment data and user evaluation data for service resources;

[0008] Based on usage environment data, determine the environmental adjustment parameters for service resources; and,

[0009] Determine the user impact parameters of service resources based on user evaluation data;

[0010] Adjust the environment adjustment parameters based on user impact parameters to update the environment adjustment parameters of service resources.

[0011] In one embodiment, adjusting environmental adjustment parameters based on user impact parameters to update the environmental adjustment parameters of service resources includes:

[0012] Based on the relationship between the user influence parameter and the preset influence threshold, the user influence parameter is adjusted to obtain the target influence parameter;

[0013] The environmental adjustment parameters of the service resources are updated based on the sum of the environmental adjustment parameters and the target impact parameters.

[0014] In one embodiment, the user influence parameter is adjusted based on the relationship between the user influence parameter and a preset influence threshold to obtain the target influence parameter, including:

[0015] If the user impact parameter is greater than the preset impact threshold, increase the user impact parameter to obtain the target impact parameter;

[0016] If the user impact parameter is not greater than the preset impact threshold, the user impact parameter is reduced to obtain the target impact parameter.

[0017] In one embodiment, the user review data includes at least one review text; based on the user review data, user impact parameters of the service resource are determined, including:

[0018] Based on the trained emotion perception model, the probability of negative emotion in different evaluation texts is determined according to user evaluation data.

[0019] Based on the negative sentiment probability of different evaluation texts and the preset text weights of the corresponding evaluation texts, the user impact parameters of service resources are determined.

[0020] In one embodiment, determining the environmental adjustment parameters for service resources based on usage environment data includes:

[0021] The environmental adjustment parameter determination model is trained based on the environmental data of the service environment to determine the environmental adjustment parameters of the service resources. The environmental adjustment parameter determination model is trained based on the gradient boosting tree model.

[0022] In one embodiment, the environmental adjustment parameter determination model is trained as follows:

[0023] Obtain sample usage environment data from sample service resources;

[0024] For each iteration, the model is determined based on the environmental adjustment parameters, and the reference environmental adjustment parameters for this iteration are determined based on the sample usage environmental data.

[0025] Determine the residual value between the reference environmental adjustment parameters and the preset standard environmental adjustment parameters;

[0026] Based on the residual values ​​and sample usage environment data, the newly added decision tree in this iteration is trained and integrated into the environment adjustment parameter determination model to update the environment adjustment parameter determination model;

[0027] The training of the model is completed once the current iteration number reaches the preset threshold number, after adjusting the environmental parameters to determine the model.

[0028] The newly added decision tree is a decision tree model obtained by training the decision tree to be trained based on the residual value and the sample usage environment data; the newly added decision tree is used to reduce the residual value between the reference environment adjustment parameters and the preset standard environment adjustment parameters in the next iteration.

[0029] Secondly, this application also provides a device for determining environmental adjustment parameters of service resources, comprising:

[0030] The acquisition module is used to acquire service resource usage environment data and user evaluation data;

[0031] The determination module is used to determine the environmental adjustment parameters of the service resource based on the usage environment data; and to determine the user impact parameters of the service resource based on the user evaluation data.

[0032] The adjustment module is used to adjust the environmental adjustment parameters according to the user impact parameters in order to update the environmental adjustment parameters of the service resources.

[0033] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0034] Obtain usage environment data and user evaluation data for service resources;

[0035] Based on usage environment data, determine the environmental adjustment parameters for service resources; and,

[0036] Determine the user impact parameters of service resources based on user evaluation data;

[0037] Adjust the environment adjustment parameters based on user impact parameters to update the environment adjustment parameters of service resources.

[0038] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0039] Obtain usage environment data and user evaluation data for service resources;

[0040] Based on usage environment data, determine the environmental adjustment parameters for service resources; and,

[0041] Determine the user impact parameters of service resources based on user evaluation data;

[0042] Adjust the environment adjustment parameters based on user impact parameters to update the environment adjustment parameters of service resources.

[0043] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0044] Obtain usage environment data and user evaluation data for service resources;

[0045] Based on usage environment data, determine the environmental adjustment parameters for service resources; and,

[0046] Determine the user impact parameters of service resources based on user evaluation data;

[0047] Adjust the environment adjustment parameters based on user impact parameters to update the environment adjustment parameters of service resources.

[0048] The aforementioned methods, devices, equipment, and media for determining environmental adjustment parameters for service resources quantify subjective, unstructured user evaluations into "user impact parameters" and use them as a dynamic feedback signal to correct "environmental adjustment parameters." Essentially, this directly maps the quality of user experience to the optimization objective of resource allocation strategies. This is equivalent to adding a negative feedback loop from the end user to a traditional automated control loop, enabling the system not only to respond to measurable changes in the physical environment but also to proactively adapt to user expectations and satisfaction. Ultimately, while ensuring system stability, it achieves continuous optimization of user satisfaction, forming a virtuous cycle of increasing accuracy and understanding of users with each use. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This embodiment provides an application environment diagram for a method of determining environmental adjustment parameters for service resources.

[0051] Figure 2 A flowchart illustrating the method for determining environmental adjustment parameters of the first type of service resource provided in this embodiment;

[0052] Figure 3 This is a flowchart illustrating an environmental adjustment parameter update step provided in this embodiment;

[0053] Figure 4This is a flowchart illustrating the steps for determining model training based on environmental adjustment parameters, as provided in this embodiment.

[0054] Figure 5 This is a structural block diagram of a service resource environmental adjustment parameter determination device provided in this embodiment;

[0055] Figure 6 This is an internal structural diagram of a computer device provided in this embodiment. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] The method for determining environmental adjustment parameters of service resources provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. The computer device acquires usage environment data and user evaluation data of the service resources; determines the environmental adjustment parameters of the service resources based on the usage environment data; and determines the user impact parameters of the service resources based on the user evaluation data; and adjusts the environmental adjustment parameters according to the user impact parameters to update the environmental adjustment parameters of the service resources. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0058] In one exemplary embodiment, such as Figure 2 As shown, a method for determining environmental adjustment parameters of service resources is provided, which can be applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps S201 to S203. Wherein:

[0059] S201 obtains usage environment data and user evaluation data for service resources.

[0060] Service resources can be understood as various resources that are relied upon or used in the process of providing services, including but not limited to hardware facilities, software applications, and human resources. These resources work together to support the delivery and operation of services.

[0061] The usage environment data refers to data related to the environment in which the service resources operate or are used. This data may include, but is not limited to, macroeconomic policies, market data (such as interest rates and market volatility), and other environmental factors directly related to service use. For example, macroeconomic data includes, but is not limited to, the Consumer Price Index (CPI) and the growth rate of broad money supply (M2). Market data includes, but is not limited to, the yield on government bonds of the same maturity, interbank certificate of deposit rates, and market volatility indices. Issuer data includes, but is not limited to, the issuer's capital adequacy ratio, non-performing loan ratio, and return on assets.

[0062] User evaluation data refers to user feedback on their experience using service resources or the quality of the service. This data may exist in text form (such as evaluations or reviews), ratings, or other forms, reflecting user satisfaction, dissatisfaction, or suggestions for improvement. For example, sentiment data is the market sentiment index generated by the dynamic spread adjustment submodule.

[0063] For example, web crawling technology can be used to capture text content related to the target bank, the banking industry as a whole, the bond market, and monetary policy from financial news (such as Xinhua News Agency and Caixin), securities research reports, and social media (such as Xueqiu and Guba) in real time, and then perform standardized operations such as text cleaning, word segmentation, and removal of stop words.

[0064] S202 determines the environmental adjustment parameters of service resources based on usage environment data, and determines the user impact parameters of service resources based on user evaluation data;

[0065] Among them, environmental adjustment parameters refer to parameters determined based on the usage environment data of service resources, used to adjust the operating status or configuration of service resources to adapt to different environmental conditions. These parameters may involve multiple aspects such as hardware settings, software configuration, and operating strategies, aiming to ensure that service resources maintain optimal operating status under different environments.

[0066] Among them, user impact parameters are determined based on user evaluation data and are used to quantify the impact of user feedback on the adjustment or optimization of service resources. These parameters reflect user satisfaction with service resources, changes in demand, or improvement suggestions, and are an important basis for continuous optimization and personalized adjustments of service resources. User evaluation data includes at least one evaluation text.

[0067] In one alternative embodiment, an environment adjustment parameter determination model is established based on the trained environment adjustment parameters, and the environment adjustment parameters of the service resources are determined according to the usage environment data; the environment adjustment parameter determination model is obtained based on a gradient boosting tree model trained on it.

[0068] In one optional embodiment, based on the trained emotion perception model, the probability of negative emotion for different evaluation texts is determined according to user evaluation data; and the user impact parameters of service resources are determined according to the probability of negative emotion for different evaluation texts and the preset text weights of the corresponding evaluation texts.

[0069] For example, this embodiment can determine the user impact parameters of service resources based on the following formula (1).

[0070] (1)

[0071] Where S is the user influence parameter, with a value range of [0.1] (a higher value indicates a more negative market sentiment), and N is the total number of evaluation texts collected within the time window. Let d be the probability of negative sentiment in the evaluation text. The text weight for evaluating the d-th segment is given.

[0072] It should be noted that the probability of negative sentiment can be determined by using a BERT model pre-trained on a large corpus as a base, and then using sentiment-labeled evaluation texts (such as "positive", "negative", and "neutral") to perform supervised fine-tuning on the pre-trained model. For each evaluation text, the model will output a sentiment polarity probability distribution, for example, P(positive) = 0.02, P(neutral) = 0.05, and P(negative) = 0.93.

[0073] It should be noted that the text weight can be determined by weighting and aggregating the sentiment probability results of all texts within a specific time window (such as 24 hours before release) to generate a comprehensive user influence parameter S, and the source weight (such as research report weight > news weight > social media weight) can be considered during aggregation.

[0074] S203 adjusts the environmental adjustment parameters based on the user impact parameters to update the environmental adjustment parameters of the service resources.

[0075] In some embodiments, a sum value between user impact parameters and environmental adjustment parameters is determined; the sum value is then used to update the environmental adjustment parameters of the service resources.

[0076] It should be noted that after determining the environmental adjustment parameters, this embodiment can also include a blockchain execution and evidence storage layer. This module ensures the immutability and automatic execution of the process, and blockchain data verification adopts general methods. The contract predefines the key parameters of this issuance (size, term) and [R_low, R_high] obtained from the off-chain prediction center. More importantly, it includes immutable final parameter determination rules, such as "pricing based on marginal interest rate" or "pricing based on weighted average interest rate." Each subscription order (price, quantity, timestamp) is encrypted and recorded on the blockchain, forming a transparent, auditable subscription record that protects business privacy.

[0077] It should be noted that after obtaining the environmental adjustment parameters, this embodiment can also continuously monitor public opinion during the on-chain book-building process. If a major negative event causes a sharp deterioration in the sentiment index, the sentiment adjustment factor changes, triggering the smart contract's dynamic adjustment mechanism for the environmental adjustment parameters. After the subscription deadline, the smart contract automatically activates, calculates the final issuance rate without human intervention based on the preset pricing rules and all on-chain subscription orders, and automatically completes the allocation of service resource shares. The entire process and results are permanently distributed and stored.

[0078] The aforementioned method for determining environmental adjustment parameters for service resources quantifies subjective, unstructured user evaluations into "user impact parameters" and uses them as a dynamic feedback signal to correct the "environmental adjustment parameters." Essentially, it directly maps the quality of user experience to the optimization objective of resource allocation strategies. This is equivalent to adding a negative feedback loop from the end user to a traditional automated control loop. This allows the system to not only respond to measurable changes in the physical environment but also proactively adapt to user expectations and satisfaction. Ultimately, while ensuring system stability, it achieves continuous optimization of user satisfaction, forming a virtuous cycle that becomes increasingly accurate and user-centric with continued use.

[0079] Figure 3 This is a flowchart illustrating the environmental adjustment parameter update steps in one embodiment. This embodiment refines the steps in the above embodiment where environmental adjustment parameters are adjusted based on user impact parameters to update the environmental adjustment parameters of service resources, including the following steps:

[0080] S301 adjusts the user influence parameters based on the relationship between the user influence parameters and the preset influence threshold to obtain the target influence parameters.

[0081] In some embodiments, the relationship between the user influence parameter and the preset influence threshold is determined; if the user influence parameter is greater than the preset influence threshold, the user influence parameter is increased to obtain the target influence parameter; if the user influence parameter is not greater than the preset influence threshold, the user influence parameter is decreased to obtain the target influence parameter.

[0082] For example, based on the following formula (2), the user influence parameter is adjusted according to the relationship between the user influence parameter and the preset influence threshold to obtain the target influence parameter.

[0083] (2)

[0084] in, Spread is the target influence parameter, S is the user influence parameter, S0 is the preset influence threshold, α is a preset or trainable adjustment coefficient, and k is a slope factor to control the sensitivity of the function. It should be noted that when S exceeds this value, spread adjustment will accelerate significantly.

[0085] S302 updates the environmental adjustment parameters of the service resources based on the sum of the environmental adjustment parameters and the target impact parameters.

[0086] In some embodiments, the environmental adjustment parameters of the service resources are updated based on the sum of the environmental adjustment parameters and the target impact parameters, according to the following formula (3).

[0087] (3)

[0088] in, R0 is the target influence parameter, and R0 is the environmental adjustment parameter. adjusted Adjust parameters for the updated environment.

[0089] In the above embodiments, when the user-influence parameter exceeds a threshold, the system amplifies its impact by increasing the parameter, resulting in a larger update magnitude for the final environmental adjustment parameter. This allows for rapid and effective correction of significant negative user experiences. Conversely, when the influence parameter does not exceed the threshold, its impact is reduced to prevent frequent system adjustments or oscillations caused by minor normal fluctuations. Finally, the update is completed by summing the environmental adjustment parameter with the processed target influence parameter. This process is equivalent to introducing an intelligent controller with dead zone and nonlinear gain into automatic control. It ensures smooth system operation within a stable range while guaranteeing decisive intervention in abnormal situations, thereby improving system responsiveness and significantly enhancing overall operational robustness.

[0090] Figure 4It is a schematic flow chart of the training steps of the environmental adjustment parameter determination model in an embodiment. In this embodiment, the training steps of the environmental adjustment parameter determination model in the above embodiment are refined, including the following steps:

[0091] S401 Obtain the sample usage environment data of the sample service resources.

[0092] S402 For each iteration process, based on the environmental adjustment parameter determination model, determine the reference environmental adjustment parameter for this iteration process according to the sample usage environment data.

[0093] S403 Determine the residual value between the reference environmental adjustment parameter and the preset standard environmental adjustment parameter.

[0094] In some embodiments, based on the following formula (4), determine the residual value between the reference environmental adjustment parameter and the preset standard environmental adjustment parameter.

[0095] (4)

[0096] Where, is the residual value, is the reference environmental adjustment parameter, preset standard environmental adjustment parameter.

[0097] S404 According to the residual value and the sample usage environment data, train the newly added decision tree in this iteration process, and fuse the newly added decision tree into the environmental adjustment parameter determination model to update the environmental adjustment parameter determination model.

[0098] Among them, the newly added decision tree is a decision tree model obtained by training the decision tree to be trained according to the residual value and the sample usage environment data; the newly added decision tree is used to reduce the residual value between the reference environmental adjustment parameter and the preset standard environmental adjustment parameter in the next iteration process.

[0099] In some embodiments, use all the training data (xi, rim) to train the newly added decision tree hm(x), and the goal of this number is to learn the pseudo-residual calculated in the previous step. For each leaf node region generated by the mth tree, calculate the optimal weight that minimizes the loss function. Add the newly added decision tree (multiplied by the learning rate v, usually 0 < v < 1) to the environmental adjustment parameter determination model, , where I(x) is the indicator function, which is 1 when x belongs to the leaf node region and 0 otherwise.

[0100] S405 Until the current iteration number reaches the preset number threshold, complete the model training of the environmental adjustment parameter determination model.

[0101] In the above embodiments, in each iteration, the system uses a newly added decision tree to fit the remaining error portion that the current model cannot accurately predict, and integrates this decision tree into the main model. This allows the model to continuously correct its prediction bias, much like an expert system that constantly learns from mistakes and makes targeted improvements. Through this "divide and conquer" strategy, the model can gradually approximate complex nonlinear relationships in an additive manner. It is particularly adept at capturing complex patterns and interaction effects implicit in the data that are difficult for simple linear models to fit. Ultimately, through the cumulative effect of multiple iterations, a highly accurate and stable strong predictive model is constructed, thereby ensuring that the environmental adjustment parameters determined by this model are more scientific and more in line with actual business needs.

[0102] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0103] Based on the same inventive concept, this application also provides an apparatus for determining environmental adjustment parameters of service resources to implement the above-described method for determining environmental adjustment parameters of service resources. The solution provided by this apparatus is similar to the solution described in the above-described method. Therefore, the specific limitations in one or more embodiments of the apparatus for determining environmental adjustment parameters of service resources provided below can be found in the limitations of the method for determining environmental adjustment parameters of service resources described above, and will not be repeated here.

[0104] In one exemplary embodiment, such as Figure 5 As shown, a device for determining environmental adjustment parameters of service resources is provided, comprising: an acquisition module 501, a determination module 502, and an adjustment module 503, wherein:

[0105] Module 501 is used to acquire service resource usage environment data and user evaluation data;

[0106] The determining module 502 is configured to determine the environmental adjustment parameters of the service resource based on the usage environment data; and to determine the user impact parameters of the service resource based on the user evaluation data.

[0107] The adjustment module 503 is used to adjust the environmental adjustment parameters according to the user impact parameters in order to update the environmental adjustment parameters of the service resources.

[0108] In some embodiments, the adjustment module 503 is further configured to adjust the user impact parameter according to the relationship between the user impact parameter and the preset impact threshold to obtain the target impact parameter; and update the environmental adjustment parameter of the service resource according to the sum of the environmental adjustment parameter and the target impact parameter.

[0109] In some embodiments, the adjustment module 503 is further configured to increase the user influence parameter to obtain the target influence parameter when the user influence parameter is greater than the preset influence threshold; and to decrease the user influence parameter to obtain the target influence parameter when the user influence parameter is not greater than the preset influence threshold.

[0110] In some embodiments, the determining module 502 is further configured to determine the negative emotion probability of different evaluation texts based on the trained emotion perception model and user evaluation data; and to determine the user impact parameters of service resources based on the negative emotion probability of different evaluation texts and the preset text weights of the corresponding evaluation texts.

[0111] In some embodiments, the determining module 502 is further configured to determine the model based on the trained environment adjustment parameters, and determine the environment adjustment parameters of the service resources according to the usage environment data; the environment adjustment parameter determining model is obtained based on the gradient boosting tree model trained.

[0112] In some embodiments, the determining module 502 is further configured to acquire sample usage environment data of sample service resources; for each iteration, determine the model based on environment adjustment parameters, determine the reference environment adjustment parameters for the current iteration based on the sample usage environment data; determine the residual value between the reference environment adjustment parameters and the preset standard environment adjustment parameters; train the newly added decision tree in the current iteration based on the residual value and the sample usage environment data, and integrate the newly added decision tree into the environment adjustment parameter determining model to update the environment adjustment parameter determining model; until the current iteration number reaches the preset number threshold, the model training of the environment adjustment parameter determining model is completed; wherein, the newly added decision tree is a decision tree model obtained by training the decision tree to be trained based on the residual value and the sample usage environment data; the newly added decision tree is used to reduce the residual value between the reference environment adjustment parameters and the preset standard environment adjustment parameters in the next iteration.

[0113] The various modules in the aforementioned service resource environmental adjustment parameter determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0114] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining environmental adjustment parameters for service resources.

[0115] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0117] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0118] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0120] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0121] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining environmental adjustment parameters for service resources, characterized in that, The method includes: Obtain usage environment data and user evaluation data for service resources; Based on the usage environment data, determine the environmental adjustment parameters for the service resources; and, Based on the user evaluation data, determine the user impact parameters of the service resource; The environmental adjustment parameters are adjusted based on the user impact parameters to update the environmental adjustment parameters of the service resources.

2. The method according to claim 1, characterized in that, The step of adjusting the environment adjustment parameters according to the user impact parameters to update the environment adjustment parameters of the service resources includes: Based on the relationship between the user influence parameter and the preset influence threshold, the user influence parameter is adjusted to obtain the target influence parameter; The environmental adjustment parameters of the service resource are updated based on the sum of the environmental adjustment parameters and the target impact parameters.

3. The method according to claim 2, characterized in that, The step of adjusting the user influence parameter based on the relationship between the user influence parameter and the preset influence threshold to obtain the target influence parameter includes: If the user influence parameter is greater than the preset influence threshold, the user influence parameter is increased to obtain the target influence parameter; If the user influence parameter is not greater than a preset influence threshold, the user influence parameter is reduced to obtain the target influence parameter.

4. The method according to claim 1, characterized in that, The user review data includes at least one review text; determining the user impact parameters of the service resource based on the user review data includes: Based on the trained emotion perception model, the probability of negative emotion in different evaluation texts is determined according to the user evaluation data. The user impact parameters of the service resource are determined based on the negative sentiment probability of the different evaluation texts and the preset text weights of the corresponding evaluation texts.

5. The method according to claim 1, characterized in that, The step of determining the environmental adjustment parameters of the service resources based on the usage environment data includes: The environmental adjustment parameter determination model, based on the trained environment data, determines the environmental adjustment parameters of the service resources; the environmental adjustment parameter determination model is trained based on a gradient boosting tree model.

6. The method according to claim 5, characterized in that, The environmental adjustment parameter determination model is trained in the following manner: Obtain sample usage environment data from sample service resources; For each iteration, the model is determined based on the environmental adjustment parameters, and the reference environmental adjustment parameters for this iteration are determined based on the environmental data used in the sample. Determine the residual value between the reference environment adjustment parameters and the preset standard environment adjustment parameters; Based on the residual value and the sample usage environment data, the newly added decision tree in this iteration process is trained, and the newly added decision tree is integrated into the environment adjustment parameter determination model to update the environment adjustment parameter determination model; The training of the model for determining the environmental adjustment parameters is completed when the current iteration number reaches the preset threshold number. The newly added decision tree is a decision tree model obtained by training the decision tree to be trained based on the residual value and the sample usage environment data; the newly added decision tree is used to reduce the residual value between the reference environment adjustment parameters and the preset standard environment adjustment parameters in the next iteration.

7. A device for determining environmental adjustment parameters of service resources, characterized in that, The device includes: The acquisition module is used to acquire service resource usage environment data and user evaluation data; The determination module is used to determine the environmental adjustment parameters of the service resource based on the usage environment data; and to determine the user impact parameters of the service resource based on the user evaluation data. The adjustment module is used to adjust the environmental adjustment parameters according to the user impact parameters in order to update the environmental adjustment parameters of the service resources.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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