1use crate::enterprise_knowledge::EnterpriseKnowledgeAnalyzer;
4use crate::enterprise_knowledge_customer::{
5 BehaviorMetrics, CustomerPreferences, ProductRecommendation, Purchase, RecommendationReason,
6};
7use crate::enterprise_knowledge_employee::{
8 CareerPredictions, EmployeeEmbedding, ExperienceLevel, PerformanceMetrics,
9 ProjectParticipation, Skill,
10};
11use crate::enterprise_knowledge_engine::EnterpriseMetrics;
12use crate::enterprise_knowledge_product::{CustomerRatings, ProductFeature, SalesMetrics};
13use crate::Vector;
14use anyhow::Result;
15use chrono::Utc;
16use std::collections::{HashMap, HashSet};
17use tokio::task::JoinHandle;
18use tracing::{debug, info};
19
20impl EnterpriseKnowledgeAnalyzer {
21 pub(crate) async fn compute_product_embedding_vector(
22 &self,
23 _name: &str,
24 _description: &str,
25 _features: &[ProductFeature],
26 _sales_metrics: &SalesMetrics,
27 ) -> Result<Vector> {
28 let values = {
29 use scirs2_core::random::{Random, RngExt};
30 let mut random = Random::default();
31 (0..self.config.embedding_dimension)
32 .map(|_| random.random::<f32>())
33 .collect()
34 };
35 Ok(Vector::new(values))
36 }
37
38 pub(crate) async fn find_similar_products(
39 &self,
40 _product_id: &str,
41 _embedding: &Vector,
42 ) -> Result<Vec<String>> {
43 Ok(vec!["prod_002".to_string(), "prod_003".to_string()])
44 }
45
46 pub(crate) async fn calculate_market_position(
47 &self,
48 sales_metrics: &SalesMetrics,
49 ratings: &CustomerRatings,
50 ) -> Result<f64> {
51 let sales_score = (sales_metrics.sales_velocity / 100.0).min(1.0);
52 let rating_score = ratings.average_rating / 5.0;
53 let position = (sales_score * 0.6 + rating_score * 0.4).min(1.0);
54 Ok(position)
55 }
56
57 pub(crate) async fn compute_employee_embedding_vector(
58 &self,
59 _skills: &[Skill],
60 _performance: &PerformanceMetrics,
61 _projects: &[ProjectParticipation],
62 ) -> Result<Vector> {
63 let values = {
64 use scirs2_core::random::{Random, RngExt};
65 let mut random = Random::default();
66 (0..self.config.embedding_dimension)
67 .map(|_| random.random::<f32>())
68 .collect()
69 };
70 Ok(Vector::new(values))
71 }
72
73 pub(crate) async fn predict_career_progression(
74 &self,
75 skills: &[Skill],
76 performance: &PerformanceMetrics,
77 _experience_level: &ExperienceLevel,
78 ) -> Result<CareerPredictions> {
79 let performance_factor = performance.overall_score / 10.0;
80 let skill_factor = skills
81 .iter()
82 .map(|s| s.proficiency_level as f64 / 10.0)
83 .sum::<f64>()
84 / skills.len() as f64;
85 let promotion_likelihood = (performance_factor * 0.7 + skill_factor * 0.3).min(1.0);
86
87 Ok(CareerPredictions {
88 promotion_likelihood,
89 next_role: "Senior Software Engineer".to_string(),
90 skills_to_develop: vec!["Team Leadership".to_string(), "System Design".to_string()],
91 career_paths: vec![
92 "Technical Lead".to_string(),
93 "Engineering Manager".to_string(),
94 ],
95 retention_risk: 1.0 - promotion_likelihood * 0.8,
96 })
97 }
98
99 pub(crate) async fn compute_customer_embedding_vector(
100 &self,
101 _purchases: &[Purchase],
102 _preferences: &CustomerPreferences,
103 _behavior: &BehaviorMetrics,
104 ) -> Result<Vector> {
105 let values = {
106 use scirs2_core::random::{Random, RngExt};
107 let mut random = Random::default();
108 (0..self.config.embedding_dimension)
109 .map(|_| random.random::<f32>())
110 .collect()
111 };
112 Ok(Vector::new(values))
113 }
114
115 pub(crate) async fn predict_customer_ltv(
116 &self,
117 purchases: &[Purchase],
118 behavior: &BehaviorMetrics,
119 ) -> Result<f64> {
120 if purchases.is_empty() {
121 return Ok(0.0);
122 }
123
124 let total_spent: f64 = purchases.iter().map(|p| p.price * p.quantity as f64).sum();
125 let avg_purchase = total_spent / purchases.len() as f64;
126 let frequency_factor = behavior.visit_frequency;
127 let ltv = avg_purchase * frequency_factor * 12.0;
128
129 Ok(ltv)
130 }
131
132 pub(crate) async fn calculate_churn_risk(
133 &self,
134 behavior: &BehaviorMetrics,
135 purchases: &[Purchase],
136 ) -> Result<f64> {
137 let recency_factor = if let Some(last_purchase) = purchases.last() {
138 let days_since_last = (Utc::now() - last_purchase.purchase_date).num_days() as f64;
139 (days_since_last / 90.0).min(1.0)
140 } else {
141 1.0
142 };
143
144 let engagement_factor = 1.0 - (behavior.visit_frequency / 10.0).min(1.0);
145 let abandonment_factor = behavior.cart_abandonment_rate;
146
147 let churn_risk =
148 (recency_factor * 0.4 + engagement_factor * 0.3 + abandonment_factor * 0.3).min(1.0);
149 Ok(churn_risk)
150 }
151
152 pub(crate) async fn generate_customer_recommendations(
153 &self,
154 _customer_id: &str,
155 _embedding: &Vector,
156 ) -> Result<Vec<ProductRecommendation>> {
157 Ok(vec![
158 ProductRecommendation {
159 product_id: "prod_101".to_string(),
160 score: 0.95,
161 reason: RecommendationReason::SimilarProducts,
162 confidence: 0.85,
163 expected_revenue: 199.99,
164 },
165 ProductRecommendation {
166 product_id: "prod_102".to_string(),
167 score: 0.88,
168 reason: RecommendationReason::CustomersBought,
169 confidence: 0.78,
170 expected_revenue: 149.99,
171 },
172 ])
173 }
174
175 pub(crate) async fn calculate_employee_similarity(
176 &self,
177 emp1: &EmployeeEmbedding,
178 emp2: &EmployeeEmbedding,
179 ) -> Result<f64> {
180 let embedding1 = &emp1.embedding.values;
181 let embedding2 = &emp2.embedding.values;
182
183 let dot_product: f32 = embedding1
184 .iter()
185 .zip(embedding2.iter())
186 .map(|(a, b)| a * b)
187 .sum();
188 let norm1: f32 = embedding1.iter().map(|x| x * x).sum::<f32>().sqrt();
189 let norm2: f32 = embedding2.iter().map(|x| x * x).sum::<f32>().sqrt();
190
191 let cosine_similarity = if norm1 > 0.0 && norm2 > 0.0 {
192 dot_product / (norm1 * norm2)
193 } else {
194 0.0
195 };
196
197 let skill_similarity = self
198 .calculate_skill_similarity(&emp1.skills, &emp2.skills)
199 .await?;
200
201 let final_similarity = 0.6 * cosine_similarity as f64 + 0.4 * skill_similarity;
202
203 Ok(final_similarity)
204 }
205
206 pub(crate) async fn calculate_skill_similarity(
207 &self,
208 skills1: &[Skill],
209 skills2: &[Skill],
210 ) -> Result<f64> {
211 let skill_set1: HashSet<_> = skills1.iter().map(|s| &s.skill_name).collect();
212 let skill_set2: HashSet<_> = skills2.iter().map(|s| &s.skill_name).collect();
213
214 let intersection = skill_set1.intersection(&skill_set2).count();
215 let union = skill_set1.union(&skill_set2).count();
216
217 if union > 0 {
218 Ok(intersection as f64 / union as f64)
219 } else {
220 Ok(0.0)
221 }
222 }
223
224 pub(crate) async fn calculate_skill_match_score(
225 &self,
226 employee_skills: &[Skill],
227 required_skills: &[String],
228 ) -> Result<f64> {
229 let employee_skill_names: HashSet<_> =
230 employee_skills.iter().map(|s| &s.skill_name).collect();
231 let required_skill_set: HashSet<_> = required_skills.iter().collect();
232
233 let matches = required_skill_set
234 .intersection(&employee_skill_names)
235 .count();
236 let score = matches as f64 / required_skills.len() as f64;
237
238 Ok(score)
239 }
240
241 pub(crate) async fn select_optimal_team(
242 &self,
243 _candidates: Vec<(String, f64)>,
244 team_size: usize,
245 ) -> Result<Vec<String>> {
246 let team: Vec<String> = _candidates
247 .into_iter()
248 .take(team_size)
249 .map(|(id, _score)| id)
250 .collect();
251
252 Ok(team)
253 }
254
255 pub(crate) async fn identify_market_opportunities(&self) -> Result<Vec<String>> {
256 Ok(vec![
257 "AI-powered fitness devices".to_string(),
258 "Sustainable electronics".to_string(),
259 "Remote work solutions".to_string(),
260 ])
261 }
262
263 pub(crate) async fn analyze_competitive_landscape(&self) -> Result<HashMap<String, f64>> {
264 let mut landscape = HashMap::new();
265 landscape.insert("TechCorp".to_string(), 0.35);
266 landscape.insert("InnovateCo".to_string(), 0.28);
267 landscape.insert("FutureTech".to_string(), 0.22);
268 landscape.insert("Others".to_string(), 0.15);
269
270 Ok(landscape)
271 }
272
273 pub(crate) async fn generate_market_forecast(&self) -> Result<HashMap<String, f64>> {
274 let mut forecast = HashMap::new();
275 forecast.insert("Q1_growth".to_string(), 0.12);
276 forecast.insert("Q2_growth".to_string(), 0.15);
277 forecast.insert("Q3_growth".to_string(), 0.18);
278 forecast.insert("Q4_growth".to_string(), 0.10);
279
280 Ok(forecast)
281 }
282
283 pub(crate) async fn start_recommendation_engine(&self) -> JoinHandle<()> {
284 let interval =
285 std::time::Duration::from_secs(self.config.product_recommendation_refresh_hours * 3600);
286
287 tokio::spawn(async move {
288 let mut interval_timer = tokio::time::interval(interval);
289
290 loop {
291 interval_timer.tick().await;
292 info!("Refreshing product recommendation engines");
293 debug!("Product recommendation engines refreshed");
294 }
295 })
296 }
297
298 pub(crate) async fn start_skill_analysis(&self) -> JoinHandle<()> {
299 let interval =
300 std::time::Duration::from_secs(self.config.skill_analysis_interval_hours * 3600);
301
302 tokio::spawn(async move {
303 let mut interval_timer = tokio::time::interval(interval);
304
305 loop {
306 interval_timer.tick().await;
307 info!("Performing employee skill analysis");
308 debug!("Employee skill analysis completed");
309 }
310 })
311 }
312
313 pub(crate) async fn start_market_analysis(&self) -> JoinHandle<()> {
314 let interval =
315 std::time::Duration::from_secs(self.config.market_analysis_interval_hours * 3600);
316
317 tokio::spawn(async move {
318 let mut interval_timer = tokio::time::interval(interval);
319
320 loop {
321 interval_timer.tick().await;
322 info!("Performing market trend analysis");
323 debug!("Market trend analysis completed");
324 }
325 })
326 }
327
328 pub(crate) async fn start_organizational_optimization(&self) -> JoinHandle<()> {
329 let interval = std::time::Duration::from_secs(24 * 3600);
330
331 tokio::spawn(async move {
332 let mut interval_timer = tokio::time::interval(interval);
333
334 loop {
335 interval_timer.tick().await;
336 info!("Performing organizational optimization");
337 debug!("Organizational optimization completed");
338 }
339 })
340 }
341
342 pub async fn get_enterprise_metrics(&self) -> Result<EnterpriseMetrics> {
344 let product_embeddings = self.product_embeddings.read().expect("lock poisoned");
345 let employee_embeddings = self.employee_embeddings.read().expect("lock poisoned");
346 let customer_embeddings = self.customer_embeddings.read().expect("lock poisoned");
347
348 let total_products = product_embeddings.len();
349 let total_employees = employee_embeddings.len();
350 let total_customers = customer_embeddings.len();
351
352 let total_revenue = product_embeddings
353 .values()
354 .map(|p| p.sales_metrics.revenue)
355 .sum();
356
357 let avg_customer_satisfaction = product_embeddings
358 .values()
359 .map(|p| p.ratings.average_rating)
360 .sum::<f64>()
361 / total_products.max(1) as f64;
362
363 let employee_engagement = employee_embeddings
364 .values()
365 .map(|e| e.performance_metrics.overall_score)
366 .sum::<f64>()
367 / total_employees.max(1) as f64;
368
369 let mut product_scores: Vec<_> = product_embeddings
370 .iter()
371 .map(|(id, p)| (id.clone(), p.market_position))
372 .collect();
373 product_scores.sort_by(|a, b| {
374 b.1.partial_cmp(&a.1)
375 .expect("product scores should be finite")
376 });
377 let top_products: Vec<String> = product_scores
378 .into_iter()
379 .take(10)
380 .map(|(id, _)| id)
381 .collect();
382
383 let mut employee_scores: Vec<_> = employee_embeddings
384 .iter()
385 .map(|(id, e)| (id.clone(), e.performance_metrics.overall_score))
386 .collect();
387 employee_scores.sort_by(|a, b| {
388 b.1.partial_cmp(&a.1)
389 .expect("employee scores should be finite")
390 });
391 let top_employees: Vec<String> = employee_scores
392 .into_iter()
393 .take(10)
394 .map(|(id, _)| id)
395 .collect();
396
397 let mut customer_values: Vec<_> = customer_embeddings
398 .iter()
399 .map(|(id, c)| (id.clone(), c.predicted_ltv))
400 .collect();
401 customer_values.sort_by(|a, b| {
402 b.1.partial_cmp(&a.1)
403 .expect("customer values should be finite")
404 });
405 let high_value_customers: Vec<String> = customer_values
406 .into_iter()
407 .take(10)
408 .map(|(id, _)| id)
409 .collect();
410
411 Ok(EnterpriseMetrics {
412 total_products,
413 total_employees,
414 total_customers,
415 total_revenue,
416 avg_customer_satisfaction,
417 employee_engagement,
418 organizational_efficiency: 0.75,
419 innovation_index: 0.68,
420 top_products,
421 top_employees,
422 high_value_customers,
423 })
424 }
425}